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处理效应估计中的综合指数AI 解读

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…

由成对互动得到的部分识别排序AI 解读

This paper considers the problem of ranking objects based on their latent merits using data from pairwise interactions. We allow for incomplete observation of these interactions and study what can be inferred about rankings in such settings. First, we show that identification of the ranking depends on a tradeoff between the tournament graph and the link function: in parametric models, such as the Bradley-Terry-Luce, rankings are point identified even with sparse graphs,…