Bias-Reduced Doubly Robust Estimation

By Karel Vermeulen, Stijn Vansteelandt. 2015

162 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2172fd75b7e6e8db47d8f709ceda45c60029363a

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In Statistics and Probability

Citations in the corpus

Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score. (2018, background): “ence rate of the doubly robust estimators in low dimension up to a lognfactor, seeRobins et al.(1994);Bang and Robins(2005);Robins et al. (2007);Cao et al.(2009);Tan(2010);Van der Laan and Rose(2011);Vermeulen and Vansteelandt (2015). However, in high dimension, the convergence rate becomes slower b”
Locally Robust Semiparametric Estimation (2016, methodology): “Also, orthogonality is sensitive to using first step estimators of precisely the objects specified in the model, as pointed out by van der Laan (2014) and Vermeulen and Vansteelandt (2015).”
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2018, methodology): “For previous analyses, propensity score and outcome regression models were employed either with main effects only (Hirano and Imbens (2002); Vermeulen and Vansteelandt (2015)) or with interaction terms manually added (Tan (2006)).”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, background): “Finally, Vermeulen and Vansteelandt (2015) focused on bias reduction, and developed an estimator which minimizes the asymptotic bias under misspecification of both working models.”
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts (2019, methodology): “In turn, both our result and Robins et al. (2008), extend to the non-parametric setting a seminal analogous result in the parametric setting by Vermeulen and Vansteelandt (2015).”