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A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings

Abhishek Chakrabortty · Guorong Dai首次提交 2022-01-03 · 修订 2024-08-14 · v3arXiv预印本

半监督与高维环境下处理效应估计的一般框架AI 解读

In this article, we aim to provide a general and complete understanding of semi-supervised (SS) causal inference for treatment effects. Specifically, we consider two such estimands: (a) the average treatment effect and (b) the quantile treatment effect, as prototype cases, in an SS setting, characterized by two available data sets: (i) a labeled data set of size $n$ , providing observations for a response and a set of high dimensional covariates, as well as a binary treatment…In this article, we aim to provide a general and complete understanding of semi-supervised (SS) causal inference for treatment effects. Specifically, we consider two such estimands: (a) the average treatment effect and (b) the quantile treatment effect, as prototype cases, in an SS setting, characterized by two available data sets: (i) a labeled data set of size $n$, providing observations for a response and a set of high dimensional covariates, as well as a binary treatment…

微观 · 因果推断高维与机器学习核、筛与局部多项式

关联记录被引 6 次 ↗OpenAlex · 核验 2026-09-30

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