More efficient transportation of causal effects using mediators
Abstract
We develop flexible semiparametric estimators of the average treatment effect transported to a target population that leverage mediators to achieve identification, and prior knowledge of population heterogeneity or summary-level statistics to achieve improved efficiency. We first propose a one-step semiparametric estimator that assumes knowledge of the baseline covariates driving the heterogeneity across populations. This approach improves efficiency by transporting only through these covariates, rather than through the full covariate set. We then introduce a collaborative one-step semiparametric estimator that also improves efficiency without requiring knowledge of which covariates drive study heterogeneity. Collectively, these estimators advance our ability to transport causal effects by improving efficiency in the presence of mediation and intermediate confounding through targeted dimension reduction strategies. We use simulation to examine finite-sample performance and apply our estimators to a large-scale housing mobility trial (MTO) and a hearing intervention trial (ACHIEVE).
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BibTeX
@misc{zhang2026more,
title = {{More efficient transportation of causal effects using mediators}},
author = {Xinyi Zhang and Kara E. Rudolph and James Russell Pike and Richard Liu and Michele Santacatterina and Iván Díaz},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31568},
eprint = {2610.31568},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31568},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - More efficient transportation of causal effects using mediators
AU - Xinyi Zhang
AU - Kara E. Rudolph
AU - James Russell Pike
AU - Richard Liu
AU - Michele Santacatterina
AU - Iván Díaz
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.31568
UR - https://arxiv.org/abs/2610.31568
AB - We develop flexible semiparametric estimators of the average treatment effect transported to a target population that leverage mediators to achieve identification, and prior knowledge of population heterogeneity or summary-level statistics to achieve improved efficiency. We first propose a one-step semiparametric estimator that assumes knowledge of the baseline covariates driving the heterogeneity across populations. This approach improves efficiency by transporting only through these covariates, rather than through the full covariate set. We then introduce a collaborative one-step semiparametric estimator that also improves efficiency without requiring knowledge of which covariates drive study heterogeneity. Collectively, these estimators advance our ability to transport causal effects by improving efficiency in the presence of mediation and intermediate confounding through targeted dimension reduction strategies. We use simulation to examine finite-sample performance and apply our estimators to a large-scale housing mobility trial (MTO) and a hearing intervention trial (ACHIEVE).
ER -