MultiCFR: Counterfactual Regression for Multimodal Treatment Effect Estimation

Existing individual treatment effect (ITE) estimation methods mainly focus on tabular covariates. However, in practice, tabular data often coexist with text and image modalities, particularly in healthcare. Leveraging such multimodal data can capture richer confounding information and enable more effective confounding adjustment. Only a few methods transfer multimodal fusion techniques to ITE estimation, but overlook explicit tabular semantics and the trade-off between distribution balancing and factual outcome prediction. To address these issues, we propose $\text{MultiCFR}$, a counterfactual regression method for multimodal ITE estimation with two key designs. (1) Tabular semantic anchoring uses explicit tabular semantics to guide relevant information extraction from text tokens and image patches. (2) Dual-branch representation learning separates the fused representation into balanced and predictive branches to mitigate confounding bias while preserving useful information for factual outcome prediction. Theoretically, we derive a generalization-error bound that characterizes the trade-off between distribution balancing and predictive information preservation. Empirically, we construct two semi-synthetic multimodal datasets for standardized evaluation, MIMIC-TIT and IHDP-TIT, where MultiCFR consistently outperforms existing baselines with lower ITE estimation errors.