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Title: Long-term causal inference under persistent confounding via data combination Authors:  Guido Imbens - Stanford University (United States)
Nathan Kallus - Cornell University (United States)
Xiaojie Mao - Tsinghua University (China)
Yuhao Wang - Tsinghua University (China) [presenting]
Abstract: The identification and estimation of long-term treatment effects are studied 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. 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.