DiPPS

Differentially Private Propensity Scores for Bias Correction

DiPPS estimates propensity scores under differential privacy to reweight a biased sample toward the true population distribution. It uses a private logistic regression model to compute importance weights, then applies them for unbiased downstream statistical estimation. The approach handles both voluntary participation bias and distribution shift from proxy datasets, with formal differential privacy guarantees.