Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
We propose a novel multi-task neural network approach for estimating
distributional treatment effects (DTE) in randomized experiments. While DTE
provides more granular insights into the experiment outcomes over conventional
methods focusing on the Average Treatment Effect (ATE), estimating it with
regression adjustment methods presents significant challenges. Specifically,
precision in the distribution tails suffers due to data imbalance, and
computational inefficiencies arise from the need to solve numerous regression
problems, particularly in large-scale datasets commonly encountered in
industry. To address these limitations, our method leverages multi-task neural
networks to estimate conditional outcome distributions while incorporating
monotonic shape constraints and multi-threshold label learning to enhance
accuracy. To demonstrate the practical effectiveness of our proposed method, we
apply our method to both simulated and real-world datasets, including a
randomized field experiment aimed at reducing water consumption in the US and a
large-scale A/B test from a leading streaming platform in Japan. The
experimental results consistently demonstrate superior performance across
various datasets, establishing our method as a robust and practical solution
for modern causal inference applications requiring a detailed understanding of
treatment effect heterogeneity.
