- A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications.
- Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation.
- Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition.
- Unbiased IoU for Spherical Image Object Detection.
- Latent Space Explanation by Intervention.
- FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos.
- LAGConv: Local-Context Adaptive Convolution Kernels with Global Harmonic Bias for Pansharpening.
- Deconfounding Physical Dynamics with Global Causal Relation and Confounder Transmission for Counterfactual Prediction.
- Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction.
- A Causal Debiasing Framework for Unsupervised Salient Object Detection.
- A Causal Inference Look at Unsupervised Video Anomaly Detection.
- Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-identification.
- Visual Sound Localization in the Wild by Cross-Modal Interference Erasing.
- Information-Theoretic Bias Reduction via Causal View of Spurious Correlation.
- MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-Based Image Captioning.
- Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification.
- Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias.
- Learning Human Driving Behaviors with Sequential Causal Imitation Learning.
- Locally Fair Partitioning.
- Fair and Truthful Giveaway Lotteries.
- Truthful and Fair Mechanisms for Matroid-Rank Valuations.
- A Little Charity Guarantees Fair Connected Graph Partitioning.
- Weighted Fairness Notions for Indivisible Items Revisited.
- Fair and Efficient Allocations of Chores under Bivalued Preferences.
- Improved Maximin Guarantees for Subadditive and Fractionally Subadditive Fair Allocation Problem.
- AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms.
- FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles.
- On Optimizing Interventions in Shared Autonomy.
- DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training.
- On Testing for Discrimination Using Causal Models.
- Reasoning about Causal Models with Infinitely Many Variables.
- Unit Selection with Causal Diagram.
- Bounds on Causal Effects and Application to High Dimensional Data.
- Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery.
- Multimodal Adversarially Learned Inference with Factorized Discriminators.
- Online Certification of Preference-Based Fairness for Personalized Recommender Systems.
- Modification-Fair Cluster Editing.
- Enhancing Counterfactual Classification Performance via Self-Training.
- Recovering the Propensity Score from Biased Positive Unlabeled Data.
- Reinforcement Learning of Causal Variables Using Mediation Analysis.
- Achieving Counterfactual Fairness for Causal Bandit.
- Causal Discovery in Hawkes Processes by Minimum Description Length.
- Group-Aware Threshold Adaptation for Fair Classification.
- Same State, Different Task: Continual Reinforcement Learning without Interference.
- Spatial Frequency Bias in Convolutional Generative Adversarial Networks.
- A Computable Definition of the Spectral Bias.
- Gradient Based Activations for Accurate Bias-Free Learning.
- Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold.
- Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling.
- Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates.
- A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data.
- Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance.
- On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints.
- On Causally Disentangled Representations.
- Knowledge Distillation via Constrained Variational Inference.
- VACA: Designing Variational Graph Autoencoders for Causal Queries.
- Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling.
- Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets.
- Controlling Underestimation Bias in Reinforcement Learning via Quasi-median Operation.
- Training a Resilient Q-network against Observational Interference.
- Early-Bird GCNs: Graph-Network Co-optimization towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets.
- Error-Based Knockoffs Inference for Controlled Feature Selection.
- Cooperative Multi-Agent Fairness and Equivariant Policies.
- MLink: Linking Black-Box Models for Collaborative Multi-Model Inference.
- Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness.
- Towards Debiasing DNN Models from Spurious Feature Influence.
- Algorithmic Fairness Verification with Graphical Models.
- Achieving Long-Term Fairness in Sequential Decision Making.
- Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values.
- On the Fairness of Causal Algorithmic Recourse.
- Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation.
- Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae.
- Fast and More Powerful Selective Inference for Sparse High-Order Interaction Model.
- Inference and Learning with Model Uncertainty in Probabilistic Logic Programs.
- Mitigating Reporting Bias in Semi-supervised Temporal Commonsense Inference with Probabilistic Soft Logic.
- Unsupervised Editing for Counterfactual Stories.
- Probing Linguistic Information for Logical Inference in Pre-trained Language Models.
- C2L: Causally Contrastive Learning for Robust Text Classification.
- Attention Biasing and Context Augmentation for Zero-Shot Control of Encoder-Decoder Transformers for Natural Language Generation.
- Span-Based Semantic Role Labeling with Argument Pruning and Second-Order Inference.
- KATG: Keyword-Bias-Aware Adversarial Text Generation for Text Classification.
- Supervising Model Attention with Human Explanations for Robust Natural Language Inference.
- Debiasing NLU Models via Causal Intervention and Counterfactual Reasoning.
- Hybrid Autoregressive Inference for Scalable Multi-Hop Explanation Regeneration.
- Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization.
- Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows.
- Interpreting Gender Bias in Neural Machine Translation: Multilingual Architecture Matters.
- Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving.
- Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search.
- FairFoody: Bringing In Fairness in Food Delivery.
- Gradual (In)Compatibility of Fairness Criteria.
- Unmasking the Mask - Evaluating Social Biases in Masked Language Models.
- CrossWalk: Fairness-Enhanced Node Representation Learning.
- Fair Conformal Predictors for Applications in Medical Imaging.
- Accurate and Scalable Gaussian Processes for Fine-Grained Air Quality Inference.
- Investigations of Performance and Bias in Human-AI Teamwork in Hiring.
- CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting.
- Fairness by "Where": A Statistically-Robust and Model-Agnostic Bi-level Learning Framework.
- Longitudinal Fairness with Censorship.
- Target Languages (vs. Inductive Biases) for Learning to Act and Plan.
- Anatomizing Bias in Facial Analysis.
- Combating Sampling Bias: A Self-Training Method in Credit Risk Models.
- Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence.
- Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations.
- Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract).
- Fine-Grained Urban Flow Inference via Normalizing Flow (Student Abstract).
- LITMUS Predictor: An AI Assistant for Building Reliable, High-Performing and Fair Multilingual NLP Systems.
- Obtaining Causal Information by Merging Datasets with MAXENT.
- Causally motivated shortcut removal using auxiliary labels.
- Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees.
- Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding.
- Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects.
- CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks.
- Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
- Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions.
- Identification in Tree-shaped Linear Structural Causal Models.
- Neural score matching for high-dimensional causal inference.
- Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference.
- GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL.
- Efficient interventional distribution learning in the PAC framework.
- Variance Minimization in the Wasserstein Space for Invariant Causal Prediction.
- Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies.
- Causal Effect Identification with Context-specific Independence Relations of Control Variables.
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- Meaningfully debugging model mistakes using conceptual counterfactual explanations.
- Minimum Cost Intervention Design for Causal Effect Identification.
- End-to-End Balancing for Causal Continuous Treatment-Effect Estimation.
- A query-optimal algorithm for finding counterfactuals.
- Causal structure-based root cause analysis of outliers.
- Entropic Causal Inference: Graph Identifiability.
- Counterfactual Transportability: A Formal Approach.
- Deep Causal Metric Learning.
- On the Adversarial Robustness of Causal Algorithmic Recourse.
- Robust Counterfactual Explanations for Tree-Based Ensembles.
- Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness.
- IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data.
- Inducing Causal Structure for Interpretable Neural Networks.
- Causal Inference Through the Structural Causal Marginal Problem.
- Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses.
- Neuron Dependency Graphs: A Causal Abstraction of Neural Networks.
- On Measuring Causal Contributions via do-interventions.
- Matching Learned Causal Effects of Neural Networks with Domain Priors.
- Tell me why! Explanations support learning relational and causal structure.
- CITRIS: Causal Identifiability from Temporal Intervened Sequences.
- Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent.
- Causal Transformer for Estimating Counterfactual Outcomes.
- Causal Conceptions of Fairness and their Consequences.
- Validating Causal Inference Methods.
- Interventional Contrastive Learning with Meta Semantic Regularizer.
- Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations.
- Scalable Computation of Causal Bounds.
- Causal Imitation Learning under Temporally Correlated Noise.
- Causal Dynamics Learning for Task-Independent State Abstraction.
- Fairness Interventions as (Dis)Incentives for Strategic Manipulation.
- Partial Counterfactual Identification from Observational and Experimental Data.
- ROCK: Causal Inference Principles for Reasoning about Commonsense Causality.
- Learning from Counterfactual Links for Link Prediction.
- Certified Robustness Against Natural Language Attacks by Causal Intervention.
- Counterfactual Prediction for Outcome-Oriented Treatments.
- Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks.
- Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space.
- Invariant Causal Representation Learning for Out-of-Distribution Generalization.
- Adversarial Robustness Through the Lens of Causality.
- Granger causal inference on DAGs identifies genomic loci regulating transcription.
- Consistent Counterfactuals for Deep Models.
- Causal Contextual Bandits with Targeted Interventions.
- Efficient Neural Causal Discovery without Acyclicity Constraints.
- Learning Temporally Causal Latent Processes from General Temporal Data.
- $\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap.
- Optimal Transport for Causal Discovery.
- Learning Causal Models from Conditional Moment Restrictions by Importance Weighting.
- BayCon: Model-agnostic Bayesian Counterfactual Generator.
- To Trust or Not To Trust Prediction Scores for Membership Inference Attacks.
- Ancestral Instrument Method for Causal Inference without Complete Knowledge.
- Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data.
- Learning Cluster Causal Diagrams: An Information-Theoretic Approach.
- Linear Combinatorial Semi-Bandit with Causally Related Rewards.
- DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework.
- ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria.
- On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges.
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- Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation.
- Learning Causal Effects on Hypergraphs.
- ML4S: Learning Causal Skeleton from Vicinal Graphs.
- DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness.
- Causal Attention for Interpretable and Generalizable Graph Classification.
- Estimating Individualized Causal Effect with Confounded Instruments.
- Causal Discovery on Non-Euclidean Data.
- Ask to Know More: Generating Counterfactual Explanations for Fake Claims.
- Precise Mobility Intervention for Epidemic Control Using Unobservable Information via Deep Reinforcement Learning.
- Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition.
- ASPIRE: Air Shipping Recommendation for E-commerce Products via Causal Inference Framework.
- Counterfactual Phenotyping with Censored Time-to-Events.
- What is the Most Effective Intervention to Increase Job Retention for this Disabled Worker?
- CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation.
- CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution.
- Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances.
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- Non-parametric inference of relational dependence.
- Learning soft interventions in complex equilibrium systems.
- On the definition and computation of causal treewidth.
- Counterfactual inference of second Opinions.
- A causal bandit approach to learning good atomic interventions in presence of unobserved confounders.
- Semiparametric causal sufficient dimension reduction of multidimensional treatments.
- Partially adaptive regularized multiple regression analysis for estimating linear causal effects.
- CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual GANs.
- Ordinal causal discovery.
- Identifiability of sparse causal effects using instrumental variables.
- Robust identifiability in linear structural equation models of causal inference.
- Causal forecasting: generalization bounds for autoregressive models.
- Intervention target estimation in the presence of latent variables.
- Bayesian federated estimation of causal effects from observational data.
- Causal discovery under a confounder blanket.
- Causal discovery with heterogeneous observational data.
- Causal inference with treatment measurement error: a nonparametric instrumental variable approach.
- A Model-Agnostic Causal Learning Framework for Recommendation using Search Data.
- CausPref: Causal Preference Learning for Out-of-Distribution Recommendation.
- Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning.
- LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm.
- Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US.
- Causal Representation Learning for Out-of-Distribution Recommendation.
- Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies.
- Causal Mediation Analysis with Hidden Confounders.
- Learning Fair Node Representations with Graph Counterfactual Fairness.
- Uncovering Causal Effects of Online Short Videos on Consumer Behaviors.
- Towards Unbiased and Robust Causal Ranking for Recommender Systems.
- A Counterfactual Modeling Framework for Churn Prediction.
- The Causal Learning of Retail Delinquency.
- Spatial-temporal Causal Inference for Partial Image-to-video Adaptation.
- Automated Storytelling via Causal, Commonsense Plot Ordering.
- Equivalent Causal Models.
- The Counterfactual NESS Definition of Causation.
- Testing Independence Between Linear Combinations for Causal Discovery.
- Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms.
- High-Confidence Off-Policy (or Counterfactual) Variance Estimation.
- The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective.
- Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder.
- Improving Causal Discovery By Optimal Bayesian Network Learning.
- Discovering Fully Oriented Causal Networks.
- PAC Learning of Causal Trees with Latent Variables.
- Meta Learning for Causal Direction.
- Agent Incentives: A Causal Perspective.
- Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization.
- On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning.
- A Generative Adversarial Framework for Bounding Confounded Causal Effects.
- Estimating Identifiable Causal Effects through Double Machine Learning.
- Instrumental Variable-based Identification for Causal Effects using Covariate Information.
- Bounding Causal Effects on Continuous Outcome.
- Sketch and Customize: A Counterfactual Story Generator.
- Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text.
- Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation.
- Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation.
- Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals.
- Clinical Trial of an AI-Augmented Intervention for HIV Prevention in Youth Experiencing Homelessness.
- Improving Causal Inference by Increasing Model Expressiveness.
- Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract).
- Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint.
- An Analysis of the Adaptation Speed of Causal Models.
- Regret Minimization for Causal Inference on Large Treatment Space.
- Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions.
- Exploiting Equality Constraints in Causal Inference.
- Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.
- Counterfactual Representation Learning with Balancing Weights.
- Budgeted and Non-Budgeted Causal Bandits.
- Differentiable Causal Discovery Under Unmeasured Confounding.
- Causal Inference with Selectively Deconfounded Data.
- Causal Modeling with Stochastic Confounders.
- Causal Autoregressive Flows.
- Causal Inference under Networked Interference and Intervention Policy Enhancement.
- Model updating after interventions paradoxically introduces bias.
- The CRISP-ML Approach to Handling Causality and Interpretability Issues in Machine Learning.
- Large-Scale Causality Discovery Analytics as a Service.
- Automated Counterfactual Generation in Financial Model Risk Management.
- Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing.
- Counterfactual Explainable Recommendation.
- Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank.
- The Skyline of Counterfactual Explanations for Machine Learning Decision Models.
- Counterfactual Review-based Recommendation.
- Top-N Recommendation with Counterfactual User Preference Simulation.
- Relation Network and Causal Reasoning for Image Captioning.
- Discovering Time-invariant Causal Structure from Temporal Data.
- CauSeR: Causal Session-based Recommendations for Handling Popularity Bias.
- Counterfactual Generative Smoothing for Imbalanced Natural Language Classification.
- SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation.
- Causally Attentive Collaborative Filtering.
- Causal-Aware Generative Imputation for Automated Underwriting.
- CausCF: Causal Collaborative Filtering for Recommendation Effect Estimation.
- CauseBox: A Causal Inference Toolbox for BenchmarkingTreatment Effect Estimators with Machine Learning Methods.
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- A Robust Algorithm to Unifying Offline Causal Inference and Online Multi-armed Bandit Learning.
- Nonlinear Causal Structure Learning for Mixed Data.
- Causal Discovery with Flow-based Conditional Density Estimation.
- Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis.
- Model-Free and Model-Based Policy Evaluation when Causality is Uncertain.
- Integer Programming for Causal Structure Learning in the Presence of Latent Variables.
- How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference.
- Valid Causal Inference with (Some) Invalid Instruments.
- Selecting Data Augmentation for Simulating Interventions.
- Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding.
- Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning.
- Generative Causal Explanations for Graph Neural Networks.
- Active Learning of Continuous-time Bayesian Networks through Interventions.
- Domain Generalization using Causal Matching.
- Necessary and sufficient conditions for causal feature selection in time series with latent common causes.
- Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction.
- Counterfactual Credit Assignment in Model-Free Reinforcement Learning.
- Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners.
- Regularizing towards Causal Invariance: Linear Models with Proxies.
- Optimal Counterfactual Explanations in Tree Ensembles.
- Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning.
- A Language for Counterfactual Generative Models.
- Safe Reinforcement Learning Using Advantage-Based Intervention.
- VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments.
- CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning.
- Interpretable Models for Granger Causality Using Self-explaining Neural Networks.
- Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks.
- Debiasing Concept-based Explanations with Causal Analysis.
- Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs.
- Counterfactual Generative Networks.
- ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning.
- Explaining the Efficacy of Counterfactually Augmented Data.
- CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers.
- Neural Networks for Learning Counterfactual G-Invariances from Single Environments.
- Representation Learning via Invariant Causal Mechanisms.
- What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation.
- Inferring Time-delayed Causal Relations in POMDPs from the Principle of Independence of Cause and Mechanism.
- A Ladder of Causal Distances.
- Neighborhood Intervention Consistency: Measuring Confidence for Knowledge Graph Link Prediction.
- Causal Discovery with Multi-Domain LiNGAM for Latent Factors.
- Dependent Multi-Task Learning with Causal Intervention for Image Captioning.
- User Retention: A Causal Approach with Triple Task Modeling.
- Ordering-Based Causal Discovery with Reinforcement Learning.
- Counterfactual Explanations for Optimization-Based Decisions in the Context of the GDPR.
- Provable Guarantees on the Robustness of Decision Rules to Causal Interventions.
- Causal Learning for Socially Responsible AI.
- If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques.
- Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract).
- An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios.
- AI for Planning Public Health Interventions.
- Combining Reinforcement Learning and Causal Models for Robotics Applications.
- Speeding Up Reinforcement Learning by Exploiting Causality in Reward Sequences.
- Stochastic Intervention for Causal Effect Estimation.
- A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks.
- Causal and Non-causal Training: A Dynamic Gap-complementary Generative Framework for Session-based Recommendation.
- Causal Models for Real Time Bidding with Repeated User Interactions.
- Causal Understanding of Fake News Dissemination on Social Media.
- DARING: Differentiable Causal Discovery with Residual Independence.
- Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning.
- A Difficulty-Aware Framework for Churn Prediction and Intervention in Games.
- Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition.
- Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System.
- Model-Based Counterfactual Synthesizer for Interpretation.
- Counterfactual Graphs for Explainable Classification of Brain Networks.
- Causal and Interpretable Rules for Time Series Analysis.
- Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach.
- MPCSL - A Modular Pipeline for Causal Structure Learning.
- Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability.
- Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber.
- Fairness in Networks: Social Capital, Information Access, and Interventions.
- Counterfactual Explanations in Explainable AI: A Tutorial.
- Causal Inference from Network Data.
- Bayesian Causal Inference for Real World Interactive Systems.
- The KDD 2021 Workshop on Causal Discovery (CD2021).
- Counterfactual Explanations Can Be Manipulated.
- Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning.
- Scalable Intervention Target Estimation in Linear Models.
- Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias.
- Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning.
- Can Information Flows Suggest Targets for Interventions in Neural Circuits?
- BayesIMP: Uncertainty Quantification for Causal Data Fusion.
- Invariant Causal Imitation Learning for Generalizable Policies.
- A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models.
- Instance-dependent Label-noise Learning under a Structural Causal Model.
- A Topological Perspective on Causal Inference.
- Robust Counterfactual Explanations on Graph Neural Networks.
- Learning Causal Semantic Representation for Out-of-Distribution Prediction.
- Nested Counterfactual Identification from Arbitrary Surrogate Experiments.
- BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery.
- Causal Identification with Matrix Equations.
- Causal Abstractions of Neural Networks.
- Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias.
- Dynamic Causal Bayesian Optimization.
- The Causal-Neural Connection: Expressiveness, Learnability, and Inference.
- Multi-task Learning of Order-Consistent Causal Graphs.
- How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
- Answering Complex Causal Queries With the Maximum Causal Set Effect.
- Causal Navigation by Continuous-time Neural Networks.
- Collaborative Causal Discovery with Atomic Interventions.
- Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders.
- Learning Treatment Effects in Panels with General Intervention Patterns.
- Sequential Causal Imitation Learning with Unobserved Confounders.
- Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable.
- Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models.
- Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables.
- Counterfactual Invariance to Spurious Correlations in Text Classification.
- Recovering Latent Causal Factor for Generalization to Distributional Shifts.
- Designing Counterfactual Generators using Deep Model Inversion.
- Causal Inference for Event Pairs in Multivariate Point Processes.
- Learning latent causal graphs via mixture oracles.
- Matching a Desired Causal State via Shift Interventions.
- Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions.
- Efficient Online Estimation of Causal Effects by Deciding What to Observe.
- Provably Efficient Causal Reinforcement Learning with Confounded Observational Data.
- Asymptotically Best Causal Effect Identification with Multi-Armed Bandits.
- Comprehensive Knowledge Distillation with Causal Intervention.
- DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks.
- Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases.
- Causal Influence Detection for Improving Efficiency in Reinforcement Learning.
- MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms.
- Causal Bandits with Unknown Graph Structure.
- Causal Effect Inference for Structured Treatments.
- A Causal Lens for Controllable Text Generation.
- Counterfactual Maximum Likelihood Estimation for Training Deep Networks.
- Unintended Selection: Persistent Qualification Rate Disparities and Interventions.
- Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation.
- Learning Generalized Gumbel-max Causal Mechanisms.
- Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game.
- Making a (Counterfactual) Difference One Rationale at a Time.
- Counterfactual Explanations in Sequential Decision Making Under Uncertainty.
- Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.
- Discovering Reliable Causal Rules.
- GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data.
- Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation.
- Causal additive models with unobserved variables.
- Compositional abstraction error and a category of causal models.
- Confidence in causal discovery with linear causal models.
- Extendability of causal graphical models: Algorithms and computational complexity.
- Inference of causal effects when control variables are unknown.
- Causal and interventional Markov boundaries.
- A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery.
- Condition number bounds for causal inference.
- Disentangling mixtures of unknown causal interventions.
- Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank.
- Interventions for Softening Can Lead to Hardening of Opinions: Evidence from a Randomized Controlled Trial.
- Personalized Treatment Selection using Causal Heterogeneity.
- Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision.
- Wait, Let's Think about Your Purchase Again: A Study on Interventions for Supporting Self-Controlled Online Purchases.
- Disentangling User Interest and Conformity for Recommendation with Causal Embedding.
- Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests.
- Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction.
- Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions.
- Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions.
- All the Wiser: Fake News Intervention Using User Reading Preferences.
- Theory-Based Causal Transfer: Integrating Instance-Level Induction and Abstract-Level Structure Learning.
- Explainable Reinforcement Learning through a Causal Lens.
- CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines.
- Integrating Overlapping Datasets Using Bivariate Causal Discovery.
- Distributionally Robust Counterfactual Risk Minimization.
- Cost-Effective Incentive Allocation via Structured Counterfactual Inference.
- A Bayesian Approach for Estimating Causal Effects from Observational Data.
- Learning Counterfactual Representations for Estimating Individual Dose-Response Curves.
- Multi-Label Causal Feature Selection.
- Causally Denoise Word Embeddings Using Half-Sibling Regression.
- A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations.
- POP ≡ POCL, Right? Complexity Results for Partial Order (Causal Link) Makespan Minimization
- A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments.
- Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift.
- Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets.
- Probabilistic Reasoning Across the Causal Hierarchy.
- Estimating Causal Effects Using Weighting-Based Estimators.
- Recovering Causal Structures from Low-Order Conditional Independencies.
- A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models.
- Learning from Interventions Using Hierarchical Policies for Safe Learning.
- Causal Knowledge Extraction through Large-Scale Text Mining.
- Algorithmic Bias in Recidivism Prediction: A Causal Perspective (Student Abstract).
- Ivy: Instrumental Variable Synthesis for Causal Inference.
- RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders.
- Model-Agnostic Counterfactual Explanations for Consequential Decisions.
- Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method.
- Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods.
- Feature relevance quantification in explainable AI: A causal problem.
- Causal Bayesian Optimization.
- Causal inference in degenerate systems: An impossibility result.
- Differentiable Causal Backdoor Discovery.
- On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge.
- Ordering-Based Causal Structure Learning in the Presence of Latent Variables.
- 2D-ATT: Causal Inference for Mobile Game Organic Installs with 2-Dimensional Attentional Neural Network.
- Intervention Recommendation for Improving Disability Employment.
- Analyzing Effectiveness of Gang Interventions using Koopman Operator Theory.
- Emotion Classification and Textual Clustering Techniques for Gang Intervention Data.
- Utilizing Social Media for Identifying Drug Addiction and Recovery Intervention.
- Causal Maps for Multi-Document Summarization.
- Causal Inference with Correlation Alignment.
- Imbalanced Time Series Classification for Flight Data Analyzing with Nonlinear Granger Causality Learning.
- CauseNet: Towards a Causality Graph Extracted from the Web.
NA
TBA
- Efficient Intervention Design for Causal Discovery with Latents.
- Learning and Sampling of Atomic Interventions from Observations.
- Causal Modeling for Fairness In Dynamical Systems.
- DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training.
- Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets.
- Causal Effect Identifiability under Partial-Observability.
- Strategic Classification is Causal Modeling in Disguise.
- Causal Structure Discovery from Distributions Arising from Mixtures of DAGs.
- Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models.
- Causal Strategic Linear Regression.
- Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery.
- Few-shot Domain Adaptation by Causal Mechanism Transfer.
- Alleviating Privacy Attacks via Causal Learning.
- Cost-effectively Identifying Causal Effects When Only Response Variable is Observable.
- Causal Inference using Gaussian Processes with Structured Latent Confounders.
- Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach.
- Invariant Causal Prediction for Block MDPs.
- CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods.
- Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health.
- Learning Disentangled Representations for CounterFactual Regression.
- Counterfactuals uncover the modular structure of deep generative models.
- Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality.
- Double Neural Counterfactual Regret Minimization.
- Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning.
- Towards Verified Robustness under Text Deletion Interventions.
- Spike-based causal inference for weight alignment.
- A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
- CoPhy: Counterfactual Learning of Physical Dynamics.
- Estimating counterfactual treatment outcomes over time through adversarially balanced representations.
- Learning The Difference That Makes A Difference With Counterfactually-Augmented Data.
- Causal Discovery with Reinforcement Learning.
- Relation-Based Counterfactual Explanations for Bayesian Network Classifiers.
- Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning.
- DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization.
- Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling.
- Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations.
- Bridging Causality and Learning: How Do They Benefit from Each Other?
- Generating Natural Counterfactual Visual Explanations.
- How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines.
- Decision Platform for Pattern Discovery and Causal Effect Estimation in Contraceptive Discontinuation.
- Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples.
- Learning causal dependencies in large-variate time series.
- Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand.
- Granger Causality Analysis based on Neural Networks Architectures for bivariate cases.
- Discovering biomedical causality by a generative Bayesian causal network under uncertainty.
- Cooperative Multi-Agent Deep Reinforcement Learning with Counterfactual Reward.
- Answering Binary Causal Questions: A Transfer Learning Based Approach.
- A Causal Look at Statistical Definitions of Discrimination.
- Heidegger: Interpretable Temporal Causal Discovery.
- Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions.
- Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments.
- Causal Inference Meets Machine Learning.
- Algorithmic recourse under imperfect causal knowledge: a probabilistic approach.
- A Causal View on Robustness of Neural Networks.
- Causal Intervention for Weakly-Supervised Semantic Segmentation.
- Deep Structural Causal Models for Tractable Counterfactual Inference.
- Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability.
- Learning to search efficiently for causally near-optimal treatments.
- A causal view of compositional zero-shot recognition.
- CASTLE: Regularization via Auxiliary Causal Graph Discovery.
- Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect.
- Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback.
- Interventional Few-Shot Learning.
- Causal analysis of Covid-19 Spread in Germany.
- Counterfactual Data Augmentation using Locally Factored Dynamics.
- Counterfactual Predictions under Runtime Confounding.
- Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models.
- Causal Estimation with Functional Confounders.
- Counterfactual Vision-and-Language Navigation: Unravelling the Unseen.
- Generative causal explanations of black-box classifiers.
- Towards practical differentially private causal graph discovery.
- Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks.
- Multi-task Causal Learning with Gaussian Processes.
- Towards Scalable Bayesian Learning of Causal DAGs.
- General Control Functions for Causal Effect Estimation from IVs.
- COT-GAN: Generating Sequential Data via Causal Optimal Transport.
- Causal Discovery in Physical Systems from Videos.
- Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning.
- General Transportability of Soft Interventions: Completeness Results.
- A polynomial-time algorithm for learning nonparametric causal graphs.
- Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models.
- How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
- Causal Imitation Learning With Unobserved Confounders.
- Investigating Gender Bias in Language Models Using Causal Mediation Analysis.
- High-recall causal discovery for autocorrelated time series with latent confounders.
- Learning Causal Effects via Weighted Empirical Risk Minimization.
- Adversarial Counterfactual Learning and Evaluation for Recommender System.
- Entropic Causal Inference: Identifiability and Finite Sample Results.
- Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs.
- Active Invariant Causal Prediction: Experiment Selection through Stability.
- Collapsing Bandits and Their Application to Public Health Intervention.
- Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks.
- End-to-End Learning and Intervention in Games.
- Decisions, Counterfactual Explanations and Strategic Behavior.
- Applications of Common Entropy for Causal Inference.
- Fair Multiple Decision Making Through Soft Interventions.
- Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding.
- Counterfactual Prediction for Bundle Treatment.
- Active Structure Learning of Causal DAGs via Directed Clique Trees.
- Reconsidering Generative Objectives For Counterfactual Reasoning.
- Differentiable Causal Discovery from Interventional Data.
- Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data.
- An Instance-Specific Algorithm for Learning the Structure of Causal Bayesian Networks Containing Latent Variables.
- Semi-supervised learning, causality, and the conditional cluster assumption.
- Regret Analysis of Bandit Problems with Causal Background Knowledge.
- Evaluation of Causal Structure Learning Algorithms via Risk Estimation.
- Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects.
- Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders.
- Causal screening in dynamical systems.
- Identifying causal effects in maximally oriented partially directed acyclic graphs.
- Measurement Dependence Inducing Latent Causal Models.
- Anchored Causal Inference in the Presence of Measurement Error.
- On Counterfactual Explanations under Predictive Multiplicity.
- Adapting Text Embeddings for Causal Inference.
- Identification and Estimation of Causal Effects Defined by Shift Interventions.
- Structure Learning for Cyclic Linear Causal Models.
- Permutation-Based Causal Structure Learning with Unknown Intervention Targets.
- Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles.
- Estimation Rates for Sparse Linear Cyclic Causal Models.
- Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals.
- Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets.
- Efficient Algorithms towards Network Intervention.
- Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants.
- Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning.
- Learning Model-Agnostic Counterfactual Explanations for Tabular Data.
- PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems.
- Learning Individual Causal Effects from Networked Observational Data.
- Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services.
- Building Causal Graphs from Medical Literature and Electronic Medical Records.
- Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines.
- Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding.
- Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time.
- Abstracting Causal Models.
- Identification of Causal Effects in the Presence of Selection Bias.
- Minimum Intervention Cover of a Causal Graph.
- Recursively Learning Causal Structures Using Regression-Based Conditional Independence Test.
- Estimating the Causal Effect from Partially Observed Time Series.
- Structural Causal Bandits with Non-Manipulable Variables.
- Efficient Counterfactual Learning from Bandit Feedback.
- Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks.
- Paraphrase Diversification Using Counterfactual Debiasing.
- Path-Specific Counterfactual Fairness.
- Counterfactual Reasoning in Observational Studies.
- Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals.
- Causal Discovery in the Presence of Missing Data.
- Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding.
- Interpretable Almost-Exact Matching for Causal Inference.
- Size of Interventional Markov Equivalence Classes in random DAG models.
- ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery.
- On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative.
- Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects.
- Parallel Gradient Boosting based Granger Causality Learning.
- A Statistical Causal Inference Method for Exploring Ultrasonics and Topological Deformations in Biological Systems.
TBA
- Bi-directional Causal Graph Learning through Weight-Sharing and Low-Rank Neural Network.
- One-Stage Deep Instrumental Variable Method for Causal Inference from Observational Data.
- Counterfactual Attention Supervision.
- Intervention-Aware Early Warning.
- User Response Driven Content Understanding with Causal Inference.
- Competitive Multi-agent Deep Reinforcement Learning with Counterfactual Thinking.
- Validating Causal Inference Models via Influence Functions.
- Deep Counterfactual Regret Minimization.
- Neural Network Attributions: A Causal Perspective.
- Sensitivity Analysis of Linear Structural Causal Models.
- Stable-Predictive Optimistic Counterfactual Regret Minimization.
- Counterfactual Visual Explanations.
- Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.
- Causal Identification under Markov Equivalence: Completeness Results.
- Classifying Treatment Responders Under Causal Effect Monotonicity.
- Bayesian Counterfactual Risk Minimization.
- Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models.
- Orthogonal Random Forest for Causal Inference.
- Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding.
- Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness.
- Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions.
- Explaining Image Classifiers by Counterfactual Generation.
- Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search.
- Counterfactual Fairness: Unidentification, Bound and Algorithm.
- Achieving Causal Fairness through Generative Adversarial Networks.
- ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions.
- Causal Discovery with Cascade Nonlinear Additive Noise Model.
- From Statistical Transportability to Estimating the Effect of Stochastic Interventions.
- Unit Selection Based on Counterfactual Logic.
- Estimating Causal Effects of Tone in Online Debates.
- Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns.
- The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations.
- Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts.
- CounterFactual Regression with Importance Sampling Weights.
- A Refined Understanding of Cost-optimal Planning with Polytree Causal Graphs.
- On Causal Identification under Markov Equivalence.
- Causal Embeddings for Recommendation: An Extended Abstract.
- Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning.
NA
- On Dynamic Network Models and Application to Causal Impact.
- Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data.
- The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis.
- Identifying Causal Effects via Context-specific Independence Relations.
- Robust Multi-agent Counterfactual Prediction.
- PC-Fairness: A Unified Framework for Measuring Causality-based Fairness.
- Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds.
- A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits.
- Likelihood-Free Overcomplete ICA and Applications In Causal Discovery.
- CXPlain: Causal Explanations for Model Interpretation under Uncertainty.
- Identification of Conditional Causal Effects under Markov Equivalence.
- Causal Confusion in Imitation Learning.
- The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data.
- Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets.
- Selecting causal brain features with a single conditional independence test per feature.
- Causal Regularization.
- Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation.
- Triad Constraints for Learning Causal Structure of Latent Variables.
- Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering.
- Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems.
- Constraint-based Causal Structure Learning with Consistent Separating Sets.
- Sample Efficient Active Learning of Causal Trees.
- Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions.
- Identifying When Effect Restoration Will Improve Estimates of Causal Effect.
- We Are Not Your Real Parents: Telling Causal from Confounded using MDL.
- Towards Identifying Causal Relation Between Instances and Labels.
- Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias.
- Causal Discovery with General Non-Linear Relationships using Non-Linear ICA.
- Stability of Linear Structural Equation Models of Causal Inference.
- Towards Robust Relational Causal Discovery.
- Beyond Structural Causal Models: Causal Constraints Models.
- Approximate Causal Abstractions.
- The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.
- Causal Inference Under Interference And Network Uncertainty.
- Object Conditioning for Causal Inference.
- On Open-Universe Causal Reasoning.
- Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention.
- Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality.
- Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplace.
- Estimating Position Bias without Intrusive Interventions.
- Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory.
- Causal Inference and Counterfactual Reasoning (3hr Tutorial).
- Optimizing Interventions via Offline Policy Evaluation: Studies in Citizen Science.
- Situation Calculus Semantics for Actual Causality.
- SELF: Structural Equational Likelihood Framework for Causal Discovery.
- Machine-Translated Knowledge Transfer for Commonsense Causal Reasoning.
- Measuring Conditional Independence by Independent Residuals: Theoretical Results and Application in Causal Discovery.
- Fairness in Decision-Making - The Causal Explanation Formula.
- Counterfactual Multi-Agent Policy Gradients.
- Combining Experts' Causal Judgments.
- Novel Exploration Techniques (NETs) for Malaria Policy Interventions.
NA
- "Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention.
- Probabilistic Causal Analysis of Social Influence.
- Causal Dependencies for Future Interest Prediction on Twitter.
- Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.
- Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems.
- Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms.
- Estimating Causal Effects on Social Networks.
TBA
- Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models.
- Budgeted Experiment Design for Causal Structure Learning.
- Detecting non-causal artifacts in multivariate linear regression models.
- Is Generator Conditioning Causally Related to GAN Performance?
- Learning Independent Causal Mechanisms.
- Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization.
- Causal Bandits with Propagating Inference.
- Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions.
- Implicit Causal Models for Genome-wide Association Studies.
- CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training.
- Actual Causality in a Logical Setting.
- Counterfactual Resimulation for Causal Analysis of Rule-Based Models.
- Causal Inference in Time Series via Supervised Learning.
- Discrete Interventions in Hawkes Processes with Applications in Invasive Species Management.
- A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams.
- Scalable Probabilistic Causal Structure Discovery.
- Mixed Causal Structure Discovery with Application to Prescriptive Pricing.
- Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements.
- Algorithmic Social Intervention.
- Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant.
NA
- Generalized Score Functions for Causal Discovery.
- On Discrimination Discovery and Removal in Ranked Data using Causal Graph.
- Structural Causal Bandits: Where to Intervene?
- Causal Discovery from Discrete Data using Hidden Compact Representation.
- Fast Estimation of Causal Interactions using Wold Processes.
- Equality of Opportunity in Classification: A Causal Approach.
- Direct Estimation of Differences in Causal Graphs.
- Maximum Causal Tsallis Entropy Imitation Learning.
- Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models.
- Experimental Design for Cost-Aware Learning of Causal Graphs.
- Multi-domain Causal Structure Learning in Linear Systems.
- Causal Inference with Noisy and Missing Covariates via Matrix Factorization.
- Causal Inference via Kernel Deviance Measures.
- Learning Plannable Representations with Causal InfoGAN.
- Identification and Estimation of Causal Effects from Dependent Data.
- Learning and Testing Causal Models with Interventions.
- Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions.
- Computationally and statistically efficient learning of causal Bayes nets using path queries.
- From Deterministic ODEs to Dynamic Structural Causal Models.
- Learning the Causal Structure of Copula Models with Latent Variables.
- Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders.
- Causal Learning for Partially Observed Stochastic Dynamical Systems.
- Identification of Personalized Effects Associated With Causal Pathways.
- Causal Discovery in the Presence of Measurement Error.
- Non-Parametric Path Analysis in Structural Causal Models.
- Estimation of Personalized Effects Associated With Causal Pathways.
- Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms.
- Causal Identification under Markov Equivalence.
- Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.
- On the Causal Effect of Badges.
- Learning Causal Effects From Many Randomized Experiments Using Regularized Instrumental Variables.
- A Short-term Intervention for Long-term Fairness in the Labor Market.
- Entropic Causal Inference.
- Causal Discovery Using Regression-Based Conditional Independence Tests.
- Informative Subspace Learning for Counterfactual Inference.
- Using Discourse Signals for Robust Instructor Intervention Prediction.
- Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks.
- Causal Effect Identification by Adjustment under Confounding and Selection Biases.
- PAG2ADMG: A Novel Methodology to Enumerate Causal Graph Structures.
- A Framework for Optimal Matching for Causal Inference.
- Learning Optimal Interventions.
- Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness.
NA
TBA
- MDL for Causal Inference on Discrete Data.
- Robust Estimation of Gaussian Copula Causal Structure from Mixed Data with Missing Values.
- Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.
- Ranking Causal Anomalies by Modeling Local Propagations on Networked Systems.
- Uncovering Causality from Multivariate Hawkes Integrated Cumulants.
- Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference.
- Fake News Mitigation via Point Process Based Intervention.
- Counterfactual Data-Fusion for Online Reinforcement Learners.
- Deep IV: A Flexible Approach for Counterfactual Prediction.
- Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics.
- Cost-Optimal Learning of Causal Graphs.
- Identifying Best Interventions through Online Importance Sampling.
NA
- A Core-Guided Approach to Learning Optimal Causal Graphs.
- Transfer Learning in Multi-Armed Bandits: A Causal Approach.
- Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.
- CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis.
- A Causal Framework for Discovering and Removing Direct and Indirect Discrimination.
- Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract).
- Probabilistic matching: Causal inference under measurement errors.
- First-order causal process for causal modelling with instantaneous and cross-temporal relations.
NA
- Avoiding Discrimination through Causal Reasoning.
- GP CaKe: Effective brain connectivity with causal kernels.
- Reliable Decision Support using Counterfactual Models.
- Learning Causal Structures Using Regression Invariance.
- Counterfactual Fairness.
- Permutation-based Causal Inference Algorithms with Interventions.
- When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.
- Causal Effect Inference with Deep Latent-Variable Models.
- Experimental Design for Learning Causal Graphs with Latent Variables.
- SAT-Based Causal Discovery under Weaker Assumptions.
- Causal Consistency of Structural Equation Models.
- Causal Discovery from Temporally Aggregated Time Series.
NA
- Constructing and Embedding Abstract Event Causality Networks from Text Snippets.
- Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering.
- Mining Medical Causality for Diagnosis Assistance.
- Counterfactual Regret Minimization in Sequential Security Games.
- Causal Explanation Under Indeterminism: A Sampling Approach.
- On Learning Causal Models from Relational Data.
- EDDIE: An Embodied AI System for Research and Intervention for Individuals with ASD.
NA
NA
TBA
- Learning Granger Causality for Hawkes Processes.
- Learning Representations for Counterfactual Inference.
NA
- Incomplete Causal Laws in the Situation Calculus Using Free Fluents.
- Situation Testing-Based Discrimination Discovery: A Causal Inference Approach.
- Intervention Strategies for Increasing Engagement in Crowdsourcing: Platform, Predictions, and Experiments.
- Causality Based Propagation History Ranking in Social Networks.
- Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.
- Towards Robust and Versatile Causal Discovery for Business Applications.
- Causal Clustering for 1-Factor Measurement Models.
- Causal Bandits: Learning Good Interventions via Causal Inference.
- Observational-Interventional Priors for Dose-Response Learning.
- Long-term Causal Effects via Behavioral Game Theory.
- Causal meets Submodular: Subset Selection with Directed Information.
- Ancestral Causal Inference.
NA
- Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data.
- Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs.
- A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics.
- Stability of Causal Inference.
- Inferring Causal Direction from Relational Data.
- On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection.
NA
NA
- A New Granger Causal Model for Influence Evolution in Dynamic Social Networks: The Case of DBLP.
- Causal Inference via Sparse Additive Models with Application to Online Advertising.
- Pearl's Causality in a Logical Setting.
- Large-Margin Multi-Label Causal Feature Learning.
- Generating Event Causality Hypotheses through Semantic Relations.
- Multi-Source Domain Adaptation: A Causal View.
- Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs.
- Recovering Causal Effects from Selection Bias.
NA
NA
NA
NA
TBA
NA
- Counterfactual Risk Minimization: Learning from Logged Bandit Feedback.
- Discovering Temporal Causal Relations from Subsampled Data.
- Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.
NA
- Differential Semantics of Intervention in Bayesian Networks.
- A Modification of the Halpern-Pearl Definition of Causality.
- Efficiently Finding Conditional Instruments for Causal Inference.
- Characterizing Causal Action Theories and Their Implementations in Answer Set Programming: Action Languages B, C, and Beyond.
- Identification of Time-Dependent Causal Model: A Gaussian Process Treatment.
- Probabilistic dynamic causal model for temporal data.
- Directed generalized measure of association: A data driven approach towards causal inference.
- Machine Learning and Causal Inference for Policy Evaluation.
- Measuring Causal Impact of Online Actions via Natural Experiments: Application to Display Advertising.
- Mining for Causal Relationships: A Data-Driven Study of the Islamic State.
- Bandits with Unobserved Confounders: A Causal Approach.
- BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions.
- Local Causal Discovery of Direct Causes and Effects.
- Learning Causal Graphs with Small Interventions.
- The Self-Normalized Estimator for Counterfactual Learning.
- Rate-Agnostic (Causal) Structure Learning.
- Estimating Ad Impact on Clicker Conversions for Causal Attribution: A Potential Outcomes Approach.
- Causal Inference by Direction of Information.
- Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information.
- Visual Causal Feature Learning.
- Learning the Structure of Causal Models with Relational and Temporal Dependence.
- Missing Data as a Causal and Probabilistic Problem.
NA
- The Computational Complexity of Structure-Based Causality.
- Recovering from Selection Bias in Causal and Statistical Inference.
- Inferring Causal Directions in Errors-in-Variables Models.
NA
TBA
NA
TBA
- Causality traces for retrospective learning in neural networks - Introduction of parallel and subjective time scales.
- Causality from Cz to C3/C4 or between C3 and C4 revealed by granger causality and new causality during motor imagery.
NA
- Causal Inference through a Witness Protection Program.
- Causal Strategic Inference in Networked Microfinance Economies.
- Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data.
- Randomized Experimental Design for Causal Graph Discovery.
- Estimating Causal Effects by Bounding Confounding.
- Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming.
NA
NA
- Causal Transportability with Limited Experiments.
- m-Transportability: Transportability of a Causal Effect from Multiple Environments.
- Meta-Transportability of Causal Effects: A Formal Approach.
- Data-driven covariate selection for nonparametric estimation of causal effects.
NA
- Causality and responsibility: probabilistic queries revisited in uncertain databases.
- Mining causal topics in text data: iterative topic modeling with time series feedback.
TBA
TBA
NA
- Sequences of Mechanisms for Causal Reasoning in Artificial Intelligence.
- Cyclic Causal Models with Discrete Variables: Markov Chain Equilibrium Semantics and Sample Ordering.
- Causal Inference with Rare Events in Large-Scale Time-Series Data.
- Multi-Dimensional Causal Discovery.
- Causal Belief Decomposition for Planning with Sensing: Completeness Results and Practical Approximation.
- Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions: Extended Abstract.
- Bayesian Probabilities for Constraint-Based Causal Discovery.
- Active learning of causal Bayesian networks using ontologies: A case study.
- Toward a causal topic model for video scene analysis.
NA
- Causal Inference on Time Series using Restricted Structural Equation Models.
- One-shot learning by inverting a compositional causal process.
- A Sound and Complete Algorithm for Learning Causal Models from Relational Data.
- Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability.
- Cyclic Causal Discovery from Continuous Equilibrium Data.
- Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure.
- From Ordinary Differential Equations to Structural Causal Models: the deterministic case.
- Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders.
- Learning Sparse Causal Models is not NP-hard.
- Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality.
- Scoring and Searching over Bayesian Networks with Causal and Associative Priors.
NA
NA
- Transportability of Causal Effects: Completeness Results.
- Generalized Sampling and Variance in Counterfactual Regret Minimization.
TBA
TBA
- 4Is of social bully filtering: identity, inference, influence, and intervention.
- InCaToMi: integrative causal topic miner between textual and non-textual time series data.
TBA
TBA
- Discovery of Causal Rules Using Partial Association.
- Granger Causality for Time-Series Anomaly Detection.
- Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs.
- On causal and anticausal learning.
TBA
TBA
- Causal discovery with scale-mixture model for spatiotemporal variance dependencies.
- Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions.
- Causal Inference by Surrogate Experiments: z-Identifiability.
- A Bayesian Approach to Constraint Based Causal Inference.
- Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables.
NA
- Relational Blocking for Causal Discovery.
- Causal Theories of Actions Revisited.
- Transportability of Causal and Statistical Relations: A Formal Approach.
- Commonsense Causal Reasoning Using Millions of Personal Stories.
- Verifying Intervention Policies to Counter Infection Propagation over Networks: A Model Checking Approach.
- Controlling Selection Bias in Causal Inference.
TBA
TBA
NA
TBA
TBA
- Using Bayesian Network Learning Algorithm to Discover Causal Relations in Multivariate Time Series.
- Causal Associative Classification.
NA
TBA
- A Logic for Causal Inference in Time Series with Discrete and Continuous Variables.
- Causal Learnability.
- Gaussianity Measures for Detecting the Direction of Causal Time Series.
NA
- The mathematics of causal inference.
- Refining causality: who copied from whom?
- Discovering spatio-temporal causal interactions in traffic data streams.
- On Causal Discovery with Cyclic Additive Noise Models.
- A rational model of causal inference with continuous causes.
NA
- A Logical Characterization of Constraint-Based Causal Discovery.
- Discovering causal structures in binary exclusive-or skew acyclic models.
- Identifiability of Causal Graphs using Functional Models.
- An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models.
- Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective.
- The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information.
- Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test.
- Kernel-based Conditional Independence Test and Application in Causal Discovery.
- Testing whether linear equations are causal: A free probability theory approach.
- Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract).
NA
NA