CauTwin: Counterfactual Contrast Supervision for Interventional Prediction in Clinical Time Series

Estimating how a patient would respond to a treatment that was not given is central to clinical decision support, and electronic health records make it possible to learn such responses from observational data. Existing methods for treatment effects over time adjust for time-varying confounding through balanced representations, inverse propensity weighting, G-computation or meta-learners, and have made effect estimates far more reliable. In intensive care, however, two problems remain. First, treatments are given to patients who are already deteriorating, so models confuse this association with the effect of the treatment and may conclude, for example, that a vasopressor lowers blood pressure. Second, each patient is observed under one treatment only, so the data never show how the effect differs between patients, and models tend to give nearly the same answer to everyone. We propose CauTwin, which learns the effect of a treatment on each patient directly. For every patient, it finds similar patients who received the other treatment and uses this comparison to guide the model's own interventional prediction. Because the comparison remains biased on average by severity that the records do not capture, CauTwin uses only how it differs between patients, which recovers how the effect varies across patients, and sets the average effect with adversarial balancing. We show that the two components compete over how the treatment may depend on severity, and that their weights control this trade-off. On three ICU cohorts, CauTwin is the only method whose vasopressor effects both point in the pharmacologically correct direction and vary with the patient, and it attains the lowest counterfactual error on a semi-synthetic tumour growth benchmark. We further show that predictive accuracy is a misleading criterion for selecting such models.