Work

Estimating Treatment Effects With Limited Exogeneity: A Machine Learning Approach to Selection Bias

Year: 2026

Type: article

Abstract: ABSTRACT This paper presents a novel method for estimating treatment effects in cases where prior knowledge of the exogeneity of the treatment variable is limited. We employ a machine learning technique, double selection via Lasso, to identify a robust set of control variables without requiring prior assumptions about their specific identities or functional forms. Our approach then leverages the principle that, under certain conditions, the selection on observables can provide bounds on the selection bias from unobservables. To illustrate the effectiveness of this method, we apply it to an empirical analysis examining the impact of legalized abortion on crime rates.

Source: International Studies of Economics

DOI: 10.1002/ise3.70028

Authors: Rui Sun, Shiyi Chen

Institutions: Mercer University, SUNY Oneonta

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $2,600