A/B Testing in Dense Large-Scale Networks: Design and Inference

Preetam Nandy, Kinjal Basu, Shaunak Chatterjee, Ye Tu

Spotlight presentation: Orals & Spotlights Track 19: Probabilistic/Causality
on 2020-12-09T07:20:00-08:00 - 2020-12-09T07:30:00-08:00
Poster Session 4 (more posters)
on 2020-12-09T09:00:00-08:00 - 2020-12-09T11:00:00-08:00
GatherTown: Causal inference and uncertainty ( Town C0 - Spot B1 )
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Abstract: Design of experiments and estimation of treatment effects in large-scale networks, in the presence of strong interference, is a challenging and important problem. Most existing methods' performance deteriorates as the density of the network increases. In this paper, we present a novel strategy for accurately estimating the causal effects of a class of treatments in a dense large-scale network. First, we design an approximate randomized controlled experiment by solving an optimization problem to allocate treatments in the presence of competition among neighboring nodes. Then we apply an importance sampling adjustment to correct for any leftover bias (from the approximation) in estimating average treatment effects. We provide theoretical guarantees, verify robustness in a simulation study, and validate the scalability and usefulness of our procedure in a real-world experiment on a large social network.

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