Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap
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稳定概率加权：有限重叠下多值处理异质性因果效应的估计与推断
- 研究问题
- 在无混杂但处理组重叠有限、倾向概率极端的情形下，如何稳定估计多值处理的异质性因果效应，并开展有限样本推断？
- 方法与思路
- 作者提出稳定概率加权及其双重稳健形式，通过条件矩模型实施大样本估计，另在细分层内构造有限样本集合估计与检验。
- 材料陈述的贡献
- 摘要称，有效的 IPW 可作为该框架的特例；还提出某种意义下无偏的有限样本集合估计，以及弱原假设检验和置信集方法。
- 阅读关注点
- 核对有限重叠下目标量是否变化、稳定加权所需条件，以及集合估计的无偏含义；检查细分层假设与有限样本有效性证明。
- 阅读边界
- 所用版本为 arXiv 预印本，同行评审或接收未核实；摘要未给出允许的重叠程度和定理条件，不能据此认定完全无重叠也可识别。
原文摘要
In this paper, I try to tame "Basu's elephants" (data with extreme selection on observables). I propose new practical large-sample and finite-sample methods for estimating and inferring heterogeneous causal effects (under unconfoundedness) in the empirically relevant context of limited overlap. I develop a general principle called "Stable Probability Weighting" (SPW) that can be used as an alternative to the widely used Inverse Probability Weighting (IPW) technique, which relies on strong overlap. I show that IPW (or its augmented version), when valid, is a special case of the more general SPW (or its doubly robust version), which adjusts for the extremeness of the conditional probabilities of the treatment states. The SPW principle can be implemented using several existing large-sample parametric, semiparametric, and nonparametric procedures for conditional moment models. In addition, I provide new finite-sample results that apply when unconfoundedness is plausible within fine strata. Since IPW estimation relies on the problematic reciprocal of the estimated propensity score, I develop a "Finite-Sample Stable Probability Weighting" (FPW) set-estimator that is unbiased in a sense. I also propose new finite-sample inference methods for testing a general class of weak null hypotheses. The associated computationally convenient methods, which can be used to construct valid confidence sets and to bound the finite-sample confidence distribution, are of independent interest. My large-sample and finite-sample frameworks extend to the setting of multivalued treatments.