Comment: Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable

Abstract

Comment on ``Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable'' [arXiv:0804.2958]

Published in at http://dx.doi.org/10.1214/07-STS227D the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• Higher order influence functions and minimax estimation of nonlinear functionals

Cited by in corpus (17)

• Entropy balancing is doubly robust
• Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis
• Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals
• Propensity score weighting under limited overlap and model misspecification
• Double Machine Learning based Program Evaluation under Unconfoundedness
• Minimal Dispersion Approximately Balancing Weights: Asymptotic Properties and Practical Considerations
• Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions
• Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
• Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects
• An intersectional framework for counterfactual fairness in risk prediction
• Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly
• Recovery and inference of causal effects with sequential adjustment for confounding and attrition
• Is checking for sequential positivity violations getting you down? Try sPoRT!
• Data integration methods for micro-randomized trials
• Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies
• A robust score test in g-computation for covariate adjustment in randomized clinical trials leveraging different variance estimators via influence functions
• Collaborative representations for targeted causal inference under outcome selection