Optimal Testing in the Experiment-rich Regime
This addresses inefficiencies in industrial A/B testing for companies conducting many experiments, though it is incremental as it builds on existing experimental design frameworks.
The authors tackled the problem of inefficient sampling in large-scale A/B testing where many hypotheses are available and observations are costly, proposing a new experimentation framework that fully characterizes the optimal policy and develops a heuristic, with simulations showing high-powered classical tests can lead to highly inefficient sampling.
Motivated by the widespread adoption of large-scale A/B testing in industry, we propose a new experimentation framework for the setting where potential experiments are abundant (i.e., many hypotheses are available to test), and observations are costly; we refer to this as the experiment-rich regime. Such scenarios require the experimenter to internalize the opportunity cost of assigning a sample to a particular experiment. We fully characterize the optimal policy and give an algorithm to compute it. Furthermore, we develop a simple heuristic that also provides intuition for the optimal policy. We use simulations based on real data to compare both the optimal algorithm and the heuristic to other natural alternative experimental design frameworks. In particular, we discuss the paradox of power: high-powered classical tests can lead to highly inefficient sampling in the experiment-rich regime.