Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption
This provides a novel complementary approach for causal inference when the mean exchangeability assumption is violated, benefiting researchers and practitioners in fields like medicine and social sciences.
The paper tackles the problem of transporting treatment effects from multiple randomized controlled trials to a target population with only control group data, by constructing a synthetic treatment group using a weighted mixture of source treatment groups, and demonstrates its effectiveness on synthetic and real-world datasets.
The purpose of this work is to transport the information from multiple randomized controlled trials to the target population where we only have the control group data. Previous works rely critically on the mean exchangeability assumption. However, as pointed out by many current studies, the mean exchangeability assumption might be violated. Motivated by the synthetic control method, we construct a synthetic treatment group for the target population by a weighted mixture of treatment groups of source populations. We estimate the weights by minimizing the conditional maximum mean discrepancy between the weighted control groups of source populations and the target population. We establish the asymptotic normality of the synthetic treatment group estimator based on the sieve semiparametric theory. Our method can serve as a novel complementary approach when the mean exchangeability assumption is violated. Experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of our methods.