Causal Inference

Ivy: Instrumental Variable Synthesis for Causal Inference AISTATS 2020 Learning Causal Effects via Weighted Empirical Risk Minimization NeurIPS 2020 Learning Counterfactual Representations for Estimating Individual Dose-Response Curves AAAI 2020 Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables JMLR 2020 Learning Optimal Cyclic Causal Graphs from Interventional Data PGM 2020 Logics of Allies and Enemies: A Formal Approach to the Dynamics of Social Balance Theory IJCAI 2020 Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution AAAI 2020 MeDIL: A Python Package for Causal Modelling PGM 2020 Off-Policy Evaluation and Learning for External Validity under a Covariate Shift NeurIPS 2020 On the Role of Sparsity and DAG Constraints for Learning Linear DAGs NeurIPS 2020 Probabilistic Reasoning Across the Causal Hierarchy AAAI 2020 Recovering Causal Structures from Low-Order Conditional Independencies AAAI 2020 Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding NeurIPS 2020 Spatio-Temporal Change Detection Using Granger Sequence Pattern IJCAI 2020 Structure Learning for Cyclic Linear Causal Models UAI 2020 Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates ACL 2020 Towards practical differentially private causal graph discovery NeurIPS 2020 Uncertainty Quantification for Inferring Hawkes Networks NeurIPS 2020 Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services AAAI 2019 A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits NeurIPS 2019 Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds NeurIPS 2019 Belief Update without Compactness in Non-finitary Languages IJCAI 2019 Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias UAI 2019 Causal Confusion in Imitation Learning NeurIPS 2019 Causal Regularization NeurIPS 2019