Abstract:Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the trial was conducted. The literature on generalizing experimental findings to new populations is extensive, yet transporting effects across time has received far less attention, and even defining the target estimand is nonobvious. We formalize the transported average treatment effect under a separable temporal effects assumption, derive two identification strategies: replicated trials and common arm, and develop doubly robust, semiparametrically efficient estimators for each. Applied to a large archive of headline A/B tests, the common arm strategy is substantially more precise but exhibits systematic bias when the temporal factor depends on the gap between intervention and measurement rather than on measurement time alone, while the replicated trials strategy, which allows this dependence, tracks the ground truth more faithfully. Simulation studies investigate when each strategy is reliable and when it silently fails.
| Subjects: | Methodology (stat.ME); Machine Learning (cs.LG); Econometrics (econ.EM) |
| Cite as: | arXiv:2603.07018 [stat.ME] |
| (or arXiv:2603.07018v2 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2603.07018 arXiv-issued DOI via DataCite |
From: Harsh Parikh [view email]
[v1]
Sat, 7 Mar 2026 03:34:13 UTC (1,078 KB)
[v2]
Tue, 19 May 2026 00:17:24 UTC (1,177 KB)
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