Treatment Effect Learning Under Sequential Randomization

This research addresses the challenges of causal inference in online experiments (A/B testing) where treatment assignments are sequential.

Core Problem:

In online settings, standard methods often fail because of carry-over effects and complex dependencies where a treatment in one session can affect outcomes in subsequent ones.

Proposed Solution:

We propose a framework that combines Meta-Learners (specifically T-Learners) with the G-Formula to handle sequential conditional exchangeability. This allows for more robust measurement of cumulative and heterogeneous treatment effects at scale.

Published on arXiv, 2025.

[Full Paper on arXiv]