Long-term Causal Inference Under Persistent Confounding via Data Combination

• URL: http://arxiv.org/abs/2202.07234v5
• Date: Sat, 31 Aug 2024 02:01:02 GMT
• Title: Long-term Causal Inference Under Persistent Confounding via Data Combination
• Authors: Guido Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang,
• Abstract summary: We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data.
• Score: 38.026740610259225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data. However, both types of data include observations of some short-term outcomes. In this paper, we uniquely tackle the challenge of persistent unmeasured confounders, i.e., some unmeasured confounders that can simultaneously affect the treatment, short-term outcomes and the long-term outcome, noting that they invalidate identification strategies in previous literature. To address this challenge, we exploit the sequential structure of multiple short-term outcomes, and develop three novel identification strategies for the average long-term treatment effect. We further propose three corresponding estimators and prove their asymptotic consistency and asymptotic normality. We finally apply our methods to estimate the effect of a job training program on long-term employment using semi-synthetic data. We numerically show that our proposals outperform existing methods that fail to handle persistent confounders.

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