KDD2025

MOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation

Yiling Liu, Wei Shi, Chen Fu, Ziyang Jiang, Zhigang Hua, David E. Carlson

摘要

Multi-treatment multi-outcome treatment effect estimation plays a vital role in today's industry-level applications. For example, in social media ads, practitioners simultaneously deploy multiple interventions to users' experience and track multi-faceted metrics (e.g., ad performance, engagement, churn). However, existing methods for estimating treatment effects struggle to simultaneously address the complex interplays and ensure robust counterfactual balancing across treatment-outcome pairs.