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Causal Inference

3,333 papers

Papers per year

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15
28
277
391
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Papers

Sequential change-point detection in high-dimensional Gaussian graphical models JMLR 2020
Joint Causal Inference from Multiple Contexts JMLR 2020
Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models JMLR 2020
Fair Data Adaptation with Quantile Preservation JMLR 2020
On Efficient Adjustment in Causal Graphs JMLR 2020
Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models L4DC 2020
Learning Dynamical Systems with Side Information L4DC 2020
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm L4DC 2020
Personalized Input-Output Hidden Markov Models for Disease Progression Modeling MLHC 2020
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model MLHC 2020
Tuning Causal Discovery Algorithms PGM 2020
Bayesian network structure learning with causal effects in the presence of latent variables PGM 2020
Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping PGM 2020
Causal Feature Learning for Utility-Maximizing Agents PGM 2020
Discovering cause-effect relationships in spatial systems with a known direction based on observational data PGM 2020
Learning Optimal Cyclic Causal Graphs from Interventional Data PGM 2020
Knowledge Transfer for Learning Markov Equivalence Classes PGM 2020
Building Causal Interaction Models by Recursive Unfolding PGM 2020
Hawkesian Graphical Event Models PGM 2020
Structural Causal Models Are (Solvable by) Credal Networks PGM 2020
CREDICI: A Java Library for Causal Inference by Credal Networks PGM 2020
CREMA: A Java Library for Credal Network Inference PGM 2020
MeDIL: A Python Package for Causal Modelling PGM 2020
SemEval-2020 Task 5: Counterfactual Recognition SEMEVAL 2020
BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models SEMEVAL 2020