Treatment Effect Learning Under Sequential Randomization
Abstract
Sequential treatment assignments in online experiments lead to complex dependency structures, often rendering identification, estimation and inference over treatments a challenge. Treatments in one session (e.g., a user logging on) can have an effect that persists into subsequent sessions, leading to cumulative effects on outcomes measured at a later stage. This can render standard methods for identification and inference trivially misspecified. We propose T-Learners layered into the G-Formula for this setting, building on literature from causal machine learning and identification in sequential settings. In a simple simulation, this approach prevents decaying accuracy in the presence of carry-over effects, highlighting the importance of identification and inference strategies tailored to the nature of systems often seen in the tech domain.
Cite
@article{arxiv.2510.20078,
title = {Treatment Effect Learning Under Sequential Randomization},
author = {Rina Friedberg and Richard Mudd and Patrick Johnstone and Melissa Pothen and Vishal Vaingankar and Vishwanath Sangale and Abbas Zaidi},
journal= {arXiv preprint arXiv:2510.20078},
year = {2025}
}