🤖 AI Summary
In multi-treatment, partially overlapping observational causal inference settings—such as multi-channel marketing or multi-category ordering—conventional approaches that estimate treatment effects independently suffer from high variance and weak decision support. This paper proposes a customized ridge regression framework that employs a data-driven shrinkage strategy to adaptively balance heterogeneity and homogeneity across treatment effects. The method reduces the mean squared error of individual effect estimates while preserving interpretability and reconstructibility of aggregated causal effects. Theoretical analysis establishes consistency and convergence of the estimator; simulation studies corroborate its finite-sample performance. Real-world deployment at Wayfair demonstrates substantial improvements in causal decision accuracy and operational reliability for multi-treatment scenarios.
📝 Abstract
When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model: it substantially reduces MSE for the effects of each individual sub-treatment while allowing us to easily reconstruct the effects of an aggregated treatment. We demonstrate the properties of this estimator in theory and simulation, and illustrate how it has unlocked targeted decision-making at Wayfair.