Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
A confounding bridge approach for double negative control inference on causal effects
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
A K-fold cross-fitted proximal bridge estimator for reward-emission and observation-transition functions in confounded POMDPs, with an oracle-comparator error bound decomposed into nuisance and averaging terms.
citing papers explorer
-
Causal Inference for All: Marginal Estimands for Outcomes Truncated by Death
Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
-
An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
-
Cross-fitted Proximal Learning for Model-Based Reinforcement Learning
A K-fold cross-fitted proximal bridge estimator for reward-emission and observation-transition functions in confounded POMDPs, with an oracle-comparator error bound decomposed into nuisance and averaging terms.