The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
van der Laan and Eric C Polley and Alan E
6 Pith papers cite this work. Polarity classification is still indexing.
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A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
PermuCATE applies conditional permutation importance to CATE estimation, claiming lower variance and higher statistical power than LOCO on simulated and health datasets.
Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.
A data-supported estimand for the effect of flexible shifts in multi-component exposure mixtures is defined and estimated nonparametrically with machine learning.
Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.
citing papers explorer
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Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
PermuCATE applies conditional permutation importance to CATE estimation, claiming lower variance and higher statistical power than LOCO on simulated and health datasets.
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Soft Learning
Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.
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Everything all at once: On choosing an estimand for multi-component environmental exposures
A data-supported estimand for the effect of flexible shifts in multi-component exposure mixtures is defined and estimated nonparametrically with machine learning.
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Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.