Causal Inference

Identification and Estimation of Causal Effects Defined by Shift Interventions UAI 2020 Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain IJCAI 2020 Integrating Deep Learning with Logic Fusion for Information Extraction AAAI 2020 Integrating Overlapping Datasets Using Bivariate Causal Discovery AAAI 2020 Invariant Causal Prediction for Block MDPs ICML 2020 Ivy: Instrumental Variable Synthesis for Causal Inference AISTATS 2020 Joint Causal Inference from Multiple Contexts JMLR 2020 Kernel-based Approach for Learning Causal Graphs from Mixed Data PGM 2020 Knowledge Transfer for Learning Markov Equivalence Classes PGM 2020 Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph EMNLP 2020 Language (Re)modelling: Towards Embodied Language Understanding ACL 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 Lee at SemEval-2020 Task 5: ALBERT Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements SemEval 2020 Local Causal Network Learning for Finding Pairs of Total and Direct Effects JMLR 2020 MeDIL: A Python Package for Causal Modelling PGM 2020 Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects NeurIPS 2020 Multi-task Causal Learning with Gaussian Processes NeurIPS 2020 NeuInfer: Knowledge Inference on N-ary Facts ACL 2020 On Efficient Adjustment in Causal Graphs JMLR 2020 On Pruning for Score-Based Bayesian Network Structure Learning AISTATS 2020 On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge AISTATS 2020 On the Role of Sparsity and DAG Constraints for Learning Linear DAGs NeurIPS 2020