Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks

TL;DR

This paper introduces a multi-task neural network approach for estimating detailed distributional treatment effects in randomized experiments, addressing data imbalance and computational challenges, and demonstrating superior performance on real-world datasets.

Contribution

The paper presents a novel multi-task neural network method that improves distributional treatment effect estimation by incorporating shape constraints and multi-threshold learning, scalable to large datasets.

Findings

Outperforms existing methods on simulated data

Achieves higher accuracy in real-world experiments

Efficiently handles large-scale datasets

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.