Double machine learning for causal inference in a multivariate sample selection model

Sofiia Dolgikh and Bodan Potanin

Papers from arXiv.org

Abstract: We propose double machine learning (DML) estimators of the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in the multivariate sample selection model with multiple ordinal selection equations. We establish asymptotic normality of the proposed estimators and study finite sample properties on simulated data. Specifically, the results of the analysis suggest that without addressing multivariate sample selection, the estimates of the causal parameters may be highly biased. However, the proposed estimators allow us to avoid these biases. Moreover, we apply the estimators to study the effect of female education on the log-wage ratio of spouses.

Date: 2025-11, Revised 2026-10
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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