Introduces a cross-fitted orthogonal hypergradient estimator derived from the efficient influence function that achieves asymptotic normality and uniform control for bilevel gradient estimation under quadratic losses.
van der Laan and Sherri Rose.Targeted Learning: Causal Inference for Observational and Experimental Data
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Digital twins from outcome models trained on historical data can function as robust synthetic controls in single-arm trials, supported by doubly robust estimators, power formulas, and reanalyses in ALS and Huntington's disease.
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
-
Semiparametric Efficient Bilevel Gradient Estimation
Introduces a cross-fitted orthogonal hypergradient estimator derived from the efficient influence function that achieves asymptotic normality and uniform control for bilevel gradient estimation under quadratic losses.
-
Digital Twins as Synthetic Controls in Single-Arm Trials
Digital twins from outcome models trained on historical data can function as robust synthetic controls in single-arm trials, supported by doubly robust estimators, power formulas, and reanalyses in ALS and Huntington's disease.
-
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.