covariate adjustment

The Price of Valid Inference After Causal Discovery UAI 2026 Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference NeurIPS 2024 Conditional Adjustment in a Markov Equivalence Class AISTATS 2024 Unified Covariate Adjustment for Causal Inference NeurIPS 2024 Using Surrogates in Covariate-adjusted Response-adaptive Randomization Experiments with Delayed Outcomes NeurIPS 2024 Causal Inference from Text: Unveiling Interactions between Variables EMNLP 2023 Estimating Possible Causal Effects with Latent Variables via Adjustment ICML 2023 A robustness test for estimating total effects with covariate adjustment UAI 2022 Deconfounded Representation Similarity for Comparison of Neural Networks NeurIPS 2022 Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency JMLR 2022 Partial Identification with Noisy Covariates: A Robust Optimization Approach CLeaR 2022 Estimating Multi-cause Treatment Effects via Single-cause Perturbation NeurIPS 2021 Machine Learning for Variance Reduction in Online Experiments NeurIPS 2021 Permutation Weighting ICML 2021 Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models JMLR 2020 On Efficient Adjustment in Causal Graphs JMLR 2020 Adjustment Criteria for Generalizing Experimental Findings ICML 2019 Finding Minimal d-separators in Linear Time and Applications UAI 2019 Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs JMLR 2018 CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions ICML 2018 Sufficient covariates and linear propensity analysis AISTATS 2010