← Core AI
Artificial Intelligence › Core AI ›

Causal Inference

3310 directly classified papers

Papers per year

Papers

Generative causal explanations of black-box classifiers NIPS 2020
Counterfactual Prediction for Bundle Treatment NIPS 2020
Recovering Causal Structures from Low-Order Conditional Independencies AAAI 2020
Machine Reasoning: Technology, Dilemma and Future EMNLP 2020
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks EMNLP 2020
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering EMNLP 2020
Characterization of Overlap in Observational Studies AISTATS 2020
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects AISTATS 2020
Learning and Sampling of Atomic Interventions from Observations ICML 2020
Causal Effect Identifiability under Partial-Observability ICML 2020
Estimation of Bounds on Potential Outcomes For Decision Making ICML 2020
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs ICML 2020
Causal Strategic Linear Regression ICML 2020
Robustness to Spurious Correlations via Human Annotations ICML 2020
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health ICML 2020
Generalized Optimal Matching Methods for Causal Inference JMLR 2020
Joint Causal Inference from Multiple Contexts JMLR 2020
Predicting the Physical Dynamics of Unseen 3D Objects WACV 2020
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding NIPS 2020
Adversarial Counterfactual Learning and Evaluation for Recommender System NIPS 2020
Entropic Causal Inference: Identifiability and Finite Sample Results NIPS 2020
Reconsidering Generative Objectives For Counterfactual Reasoning NIPS 2020
Visual Commonsense R-CNN CVPR 2020
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing CVPR 2020
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning NIPS 2020