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.

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Publication Details

Published
2025-08-03
DOI
https://doi.org/10.1145/3711896.3737056
Primary Topic
Advanced Causal Inference Techniques
Type
article
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article

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

Ziyang Jiang, Wei Shi, David Carlson, Yiling Liu et al.
Advanced Causal Inference Techniques
article

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

Ziyang Jiang, Wei Shi, David Carlson, Yiling Liu, Zhigang Hua, Chen Fu
article en

Abstract

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.

Duke University (US), Meta (United States) (US), Aqua Metrology Systems (United States) (US)
Openalex Percentile: Top 81%
Advanced Causal Inference Techniques
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