Long-term Causal Inference Under Persistent Confounding via Data Combination

By Guido W. Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang. 2022

64 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a62852385c98336cd4a62378bbaa7604c277170e

Citations in the corpus

• Using Experiments to Correct for Selection in Observational Studies (2020, unknown): “In addition to the literature on statistical surrogates, our analysis relates to other studies that have examined similar observation schemes, including Rosenman et al. [2018, 2020], Kallus and Mao [2020], Mealli and Pacini [2013] and Imbens et al. [2025].”