MOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation
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. In our paper, we propose MOTTO (Multi-Outcome, Multi-Treat-ment Transfer Model for Treatment Effect Estimation), a Mixture-of-Experts-based framework designed to jointly handle these multi-faceted challenges. MOTTO explicitly learns the relationships across outcomes and distribution overlaps across treatment groups by isolating shared experts within its partitioned architecture. It then employs selective distribution alignment to these treatment-shared experts, optimizing the balance between factual and counterfactual predictions. Importantly, MOTTO can scale to a large number of treatments and outcomes without significantly increasing the number of parameters, as its shared experts can be primarily used for efficiency with comparable performance. We validate MOTTO on synthetic data, demonstrating MOTTO's strong adaptability to varying levels of outcome correlations and confounding. We further highlight its performance improvements across real-world benchmarks and a large-scale advertising ecosystem on one of the world's largest social media platforms.
