Learning Outcomes of Joint Interventions via Sim2Real Causal Foundation Models

Real-world decisions rarely involve a single lever. Drug combinations, multiplexed gene perturbations, and multi-component policies require predicting outcomes when several variables are manipulated at once, often with limited or no interventional data. Motivated by these demands, we propose a new paradigm: learning outcomes of joint interventions (OJIs) with a single pre-trained model that infers them from observational data alone and flexibly exploits interventional data when available. In their original implementations, causal foundation models (CFMs) such as CausalPFN and Do-PFN address single-variable interventions using observational-only pre-training contexts. We realize this paradigm with JOINT (Joint Outcome Inference via Neural Transfer), a CFM pre-trained entirely on diverse causal systems simulated via next-token generation. JOINT is pre-trained on contexts that randomly include interventional samples to align pre-training with deployment, reducing mean average treatment effect (ATE) error by approximately 39% relative to observational-only pre-training when interventional examples are available. For interventions on up to ten variables, JOINT estimates individual treatment effects more accurately and achieves lower ATE error than competing causal and tabular foundation models. It remains robust under partial observability and generalizes to graphs twice the size of those seen during pre-training. Without fine-tuning, JOINT attains the lowest ATE error on real-world genetic and chemical perturbation datasets, improving on the strongest baseline by approximately 13% and demonstrating effective Sim2Real transfer.