Adapting causal effect foundation model from binary to continuous treatments

Causal effects describe how outcomes differ under alternative interventions and play an important role in medicine, economics, and public policy. Accurate causal effect estimation is therefore essential for informed decision-making. Recently, several studies have introduced causal effect foundation models based on in-context learning, achieving promising performance in causal effect estimation on new tasks without updating the parameters of the foundation model. However, existing causal effect estimation foundation models are primarily designed for binary treatments and cannot be directly applied to continuous treatment settings. In this paper, to leverage the strong predictive capability of causal effect foundation models for binary treatments in continuous treatment settings, we propose a lightweight trainable plugin to adapt a frozen causal effect foundation model of binary treatments to continuous treatment effect estimation. Specifically, we use treatment quantiles as thresholds to binarize the continuous treatment, yielding a series of anchor treatment values. For each anchor, we learn a covariate–treatment interaction representation through a task-specific encoder and feed it into a binary-treatment foundation model, whose parameters are kept fixed while the encoder is fine-tuned. Since each anchor model is expected to provide more accurate counterfactual estimates for target treatments close to its corresponding anchor value, we aggregate predictions from multiple anchor models to obtain the final prediction, with larger weights assigned to anchors closer to the target treatment. We conduct experiments on synthetic and semi-synthetic benchmark datasets to demonstrate the effectiveness of our method for continuous treatment effect estimation.