Causal Foundation Models with Continuous Treatments

• URL: http://arxiv.org/abs/2605.15133v1
• Date: Thu, 14 May 2026 17:40:28 GMT
• Title: Causal Foundation Models with Continuous Treatments
• Abstract summary: We present the first causal foundation model for the continuous treatment setting.<n>Our model meta-learns the ability to predict causal effects across a variety of unseen tasks without additional training or fine-tuning.
• Score: 15.472291460830087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variety of domains is the continuous treatment setting, where the variable of intervention has a continuous range. This setting is far less explored and represents a substantial shift from the binary treatment setting, with models needing to represent effects across a continuum of treatment values. In this paper, we present the first causal foundation model for the continuous treatment setting. Our model meta-learns the ability to predict causal effects across a wide variety of unseen tasks without additional training or fine-tuning. First, we design a novel prior over data-generating processes with continuous treatment variables in order to generate a rich causal training corpus. We then train a transformer to reconstruct individual treatment-response curves given only observational data, leveraging in-context learning to amortize expensive Bayesian posterior inference. Our model achieves state-of-the-art performance on individual treatment-response curve reconstruction tasks compared to causal models which are trained specifically for those tasks.

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