Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates via Generalized Riesz Regression
This work addresses treatment effect estimation for researchers in causal inference, offering incremental improvements through the use of auxiliary data.
This study tackles treatment effect estimation in semi-supervised settings by incorporating unlabeled auxiliary covariates, developing efficiency bounds and estimators that achieve lower asymptotic variance compared to methods without such covariates.
This study investigates treatment effect estimation in the semi-supervised setting, where we can use not only the standard triple of covariates, treatment indicator, and outcome, but also unlabeled auxiliary covariates. For this problem, we develop efficiency bounds and efficient estimators whose asymptotic variance aligns with the efficiency bound. In the analysis, we introduce two different data-generating processes: the one-sample setting and the two-sample setting. The one-sample setting considers the case where we can observe treatment indicators and outcomes for a part of the dataset, which is also called the censoring setting. In contrast, the two-sample setting considers two independent datasets with labeled and unlabeled data, which is also called the case-control setting or the stratified setting. In both settings, we find that by incorporating auxiliary covariates, we can lower the efficiency bound and obtain an estimator with an asymptotic variance smaller than that without such auxiliary covariates.