Winner’s Curse: Bias Estimation for Total Effects of Features in Online Controlled Experiments

Minyong Lee (Airbnb); Milan Shen (Airbnb)

Online controlled experiments, or A/B testing, has been a standard framework adopted by most online product companies to measure the effect of any new change. Companies use various statistical methods including hypothesis testing and statistical inference to quantify the business impact of the changes and make product decisions. Nowadays, experimentation platforms can run as many as hundreds or even more experiments concurrently. When a group of experiments is conducted, usually the ones with significant successful results are chosen to be launched into the product. We are interested in learning the aggregated impact of the launched features. In this paper, we investigate a statistical selection bias in this process and propose a correction method of getting an unbiased estimator. Moreover, we give an implementation example at Airbnb’s ERF platform (Experiment Reporting Framework) and discuss the best practices to account for this bias.

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