Causal Inference

A Logic-Driven Framework for Consistency of Neural Models EMNLP 2019 An Algorithm to Learn Polytree Networks with Hidden Nodes NeurIPS 2019 A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects AISTATS 2019 Approximate Causal Abstractions UAI 2019 Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS 2019 ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions IJCAI 2019 Beyond Structural Causal Models: Causal Constraints Models UAI 2019 Building Causal Graphs from Medical Literature and Electronic Medical Records AAAI 2019 Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias UAI 2019 Causal Discovery in the Presence of Missing Data AISTATS 2019 Causal Discovery with Cascade Nonlinear Additive Noise Model IJCAI 2019 Causal Discovery with General Non-Linear Relationships using Non-Linear ICA UAI 2019 Causal Embeddings for Recommendation: An Extended Abstract IJCAI 2019 Causal Inference Under Interference And Network Uncertainty UAI 2019 Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions NeurIPS 2019 Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge ACL 2019 Commonsense inference in human-robot communication EMNLP 2019 Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report EMNLP 2019 Constraint-based Causal Structure Learning with Consistent Separating Sets NeurIPS 2019 Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding AAAI 2019 Counterfactual Reasoning in Observational Studies AAAI 2019 CounterFactual Regression with Importance Sampling Weights IJCAI 2019 Counting and Sampling from Markov Equivalent DAGs Using Clique Trees AAAI 2019 Counting and Sampling Markov Equivalent Directed Acyclic Graphs AAAI 2019 Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations EMNLP 2019