Causal Inference

Uncertain Decisions Facilitate Better Preference Learning NeurIPS 2021 Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects AISTATS 2021 A Bayesian Approach for Estimating Causal Effects from Observational Data AAAI 2020 algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD JMLR 2020 Algorithmic recourse under imperfect causal knowledge: a probabilistic approach NeurIPS 2020 A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms ICLR 2020 A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models AAAI 2020 Bayesian network structure learning with causal effects in the presence of latent variables PGM 2020 Building Causal Interaction Models by Recursive Unfolding PGM 2020 CASTLE: Regularization via Auxiliary Causal Graph Discovery NeurIPS 2020 Causal Bayesian Optimization AISTATS 2020 Causal Discovery from Heterogeneous/Nonstationary Data JMLR 2020 Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning NeurIPS 2020 Causal Discovery in Physical Systems from Videos NeurIPS 2020 Causal Discovery Toolbox: Uncovering causal relationships in Python JMLR 2020 Causal Estimation with Functional Confounders NeurIPS 2020 Causal Feature Learning for Utility-Maximizing Agents PGM 2020 Causal inference in degenerate systems: An impossibility result AISTATS 2020 Causal Inference of Script Knowledge EMNLP 2020 Cause-Effect Association between Event Pairs in Event Datasets IJCAI 2020 Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe NeurIPS 2020 Commonsense Reasoning for Natural Language Processing ACL 2020 Conditional Causal Relationships between Emotions and Causes in Texts EMNLP 2020 Constraing-Based Learning for Continous-Time Bayesian Networks PGM 2020 Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models ICML 2020