Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data

ArXi:2606.01539v1 Announce Type: cross Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly when outcomes are rare. While g-formula-based methods such as the iterative conditional expectation (ICE) estimator provide a principled framework for longitudinal causal inference, they become computationally expensive, especially when bootstrap-based variance estimation is required.