Treatment Effect Learning Under Sequential Randomization

TL;DR

This paper introduces a new method for estimating treatment effects in sequential online experiments with persistent effects, addressing challenges of dependency and carry-over effects that standard methods struggle with.

Contribution

It proposes T-Learners integrated into the G-Formula to improve identification and inference in complex sequential treatment settings with carry-over effects.

Findings

Prevents decay in accuracy with carry-over effects

Highlights importance of tailored identification strategies

Demonstrates effectiveness through simulation

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.