CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
This study addresses the non-uniqueness of causal models derived solely from observational data and the frequent absence of target interventional experiments. To overcome these limitations, this work proposes CIDER-FM, a framework that integrates observational and surrogate interventional data to predict target conditional interventional distributions (CIDs). The approach formalizes the concept of surrogate experiments by introducing intervention-aware representations and a hierarchical three-axis attention mechanism, while constructing a causal foundation model grounded in synthetic structural causal model priors. Extensive evaluations across multiple benchmarks demonstrate superior CID prediction performance. The results confirm that incorporating experimental context significantly outperforms purely observational modeling, thereby establishing a novel paradigm for causal inference.