处理效应估计中的汇总指数
Summary Indices in Treatment Effect Estimation
浏览论文内容
中文总结 AI 辅助
本文研究处理效应估计中汇总指数的权重性质，发现逆协方差加权指数权重可为负且无界，导致指数效应符号与分量相反，并提出两种有效推断方法，且汇总指数不必然提高功效。
中文摘要 AI 辅助
本文研究了将多个结果合并为一个汇总指数以估计因果效应的实践。对于常见的估计量和指数构造方式，该估计值等于各分量效应估计值的加权和，其中权重是隐式的且很少被报告。本文推导了这些权重，并表明对于逆协方差加权指数，权重可能为负且大小不受限制，因此指数效应可能与每个分量效应的符号相反。本文提出了两种对指数效应进行有效推断的程序：一种考虑数据依赖权重的方差估计器，以及一种无需此类校正的平移t检验。对无效应零假设的常规t检验仍然有效。与常见说法相反，汇总指数通常不会提高检验功效。三项已发表的研究说明了这些结果。
英文摘要
This paper studies the practice of combining multiple outcomes into a summary index to estimate a causal effect. For common estimators and index constructions, the estimate equals a weighted sum of the estimated effects on the components, with weights that are implicit and rarely reported. The paper derives the weights and shows that, for inverse-covariance-weighted indices, they can be negative and unrestricted in magnitude, so the index effect can have the opposite sign to every component effect. The paper proposes two procedures for valid inference on the index effect: a variance estimator that accounts for the data-dependent weights, and a shifted t-test that requires no such correction. Conventional t-tests of the null of no effect remain valid. Contrary to common claims, summary indices do not generally improve power. Three published studies illustrate the results.
发表机构
- The University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。
