Causal Foundation Models with Continuous Treatments
It addresses the underexplored problem of causal inference with continuous treatments, providing a foundation model that generalizes across tasks for practitioners in domains like medicine and economics.
This paper introduces the first causal foundation model for continuous treatments, which meta-learns to predict causal effects across unseen tasks without additional training. The model achieves state-of-the-art performance on individual treatment-response curve reconstruction tasks compared to task-specific causal models.
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.