Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores

• 成果类型: Article
• 署名作者: Yang, S.; Ding, P.
• 署名单位: North Carolina State University; University of California System; University of California Berkeley
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asy008
• 发表日期: 2018
• 页码: 487493
• 关键词: doubly robust estimation; matching methods; population; overlap

摘要

Causal inference with observational studies often relies on the assumptions of unconfoundedness and overlap of covariate distributions in different treatment groups. The overlap assumption is violated when some units have propensity scores close to 0 or 1, so both practical and theoretical researchers suggest dropping units with extreme estimated propensity scores. However, existing trimming methods often do not incorporate the uncertainty in this design stage and restrict inference to only the trimmed sample, due to the nonsmoothness of the trimming. We propose a smooth weighting, which approximates sample trimming and has better asymptotic properties. An advantage of our estimator is its asymptotic linearity, which ensures that the bootstrap can be used to make inference for the target population, incorporating uncertainty arising from both design and analysis stages. We extend the theory to the average treatment effect on the treated, suggesting trimming samples with estimated propensity scores close to 1.