Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data
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. In addition, outcome rarity at each time point induces severe class imbalance, leading to instability and convergence issues in logistic regression and related models. To address these challenges, we propose a principled subsampling and reweighting strategy fo...
| Authors | Xiaohui Yin, Avijit Mitra, Ying Zhou et al. |
|---|---|
| Primary category | stat.ME |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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