Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

By J. Lunceford. 2017

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Citations in the corpus

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “(4) Inverse Probability of Treatment Weighting (IPTW) assigns a weight to each unit based on the propensity score (Lunceford and Davidian, 2004), wi = ti/π̂i + (1− ti)/(1− π̂i), (5) thus emphasizing, for example, treated units that were originally unlikely to be treated (ti = 1, low πi).”
Policy Evaluation with Latent Confounders via Optimal Balance (2019, background): “Since these weights can be extreme, both normalization [2, 31, 41] and clipping [10, 12, 40] are often employed.”
Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores (2018, unknown): “…μ(a, X ) = E(Y | A = a, X ) by μ̂(a, X ) and obtain the residual R̂i = Yi − μ̂(Ai, Xi), then the augmented weighting estimator is N −1 ∑N i=1 τ̂ aug(Xi) (Lunceford & Davidian, 2004; Bang & Robins, 2005), where τ̂ aug(Xi) = { AiR̂i e(X Ti θ̂ ) + μ̂(1, Xi) } − { (1 − Ai)R̂i 1 − e(X Ti θ̂ ) +…”
Nonparametric estimation of causal heterogeneity under high-dimensional confounding (2019, result): “In line with other results in the literature on average effects (Hirano, Imbens, and Ridder, 2003, Robins, Rotnitzky, and Zhao, 1994, Lunceford and Davidian, 2004), they show that the variance for Inverse Probability Weighting (IPW) estimators can be substantially decreased when the propensity…”
Equality of Opportunity in Classification: A Causal Approach (2018, methodology): “We provide IPW estimators for counterfactual indirect and spurious ERs in Appendix A [27].”