Addressing Hidden Imperfections in Online Experimentation

TL;DR

This paper highlights the various hidden imperfections in online randomized controlled trials used by tech companies, emphasizing their impact on bias, statistical power, and the interpretation of causal effects.

Contribution

It provides a comprehensive overview of the unseen biases and challenges in online experimentation, aiming to increase practitioner awareness and improve RCT reliability.

Findings

Identification of common biases in online RCTs

Discussion of how imperfections affect causal estimates

Recommendations for practitioners to mitigate biases

Abstract

Technology companies are increasingly using randomized controlled trials (RCTs) as part of their development process. Despite having fine control over engineering systems and data instrumentation, these RCTs can still be imperfectly executed. In fact, online experimentation suffers from many of the same biases seen in biomedical RCTs including opt-in and user activity bias, selection bias, non-compliance with the treatment, and more generally, challenges in the ability to test the question of interest. The result of these imperfections can lead to a bias in the estimated causal effect, a loss in statistical power, an attenuation of the effect, or even a need to reframe the question that can be answered. This paper aims to make practitioners of experimentation more aware of imperfections in technology-industry RCTs, which can be hidden throughout the engineering stack or in the design process.