Causal Inference

Chinese Tense Labelling and Causal Analysis COLING 2016 Compressing Bayes Net CPTs with Persistent Leaky Causes PGM 2016 Computing Lower and Upper Bounds on the Probability of Causal Statements PGM 2016 Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications ICML 2016 Conditional Independencies under the Algorithmic Independence of Conditionals JMLR 2016 Content-based Influence Modeling for Opinion Behavior Prediction COLING 2016 Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models JMLR 2016 Deep Survival Analysis MLHC 2016 Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks JMLR 2016 Estimating Causal Effects with Ancestral Graph Markov Models PGM 2016 Estimating Causal Structure Using Conditional DAG Models JMLR 2016 Identification and Overidentification of Linear Structural Equation Models NeurIPS 2016 Joint Inference for Event Coreference Resolution COLING 2016 Learning Acyclic Directed Mixed Graphs from Observations and Interventions PGM 2016 Learning Bayesian networks with ancestral constraints NeurIPS 2016 Learning Granger Causality for Hawkes Processes ICML 2016 Learning Influence Functions from Incomplete Observations NeurIPS 2016 Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation JMLR 2016 Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview JMLR 2016 Learning Representations for Counterfactual Inference ICML 2016 Multi-Level Cause-Effect Systems AISTATS 2016 Mutual Information Based Matching for Causal Inference with Observational Data JMLR 2016 New Liftable Classes for First-Order Probabilistic Inference NeurIPS 2016 Non-linear Causal Inference using Gaussianity Measures JMLR 2016 Private Causal Inference AISTATS 2016