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.
Authors
- Ziyang Jiang (ORCID: https://orcid.org/0000-0001-7096-4612)
- Wei Shi (ORCID: https://orcid.org/0000-0001-9106-1092)
- David Carlson (ORCID: https://orcid.org/0000-0003-1005-6385)
- Yiling Liu (ORCID: https://orcid.org/0000-0002-6872-0106)
- Zhigang Hua
- Chen Fu
Institutions
- Duke University (US)
- Meta (United States) (US)
- Aqua Metrology Systems (United States) (US)
Publication Details
- Published
- 2025-08-03
- DOI
- https://doi.org/10.1145/3711896.3737056
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00