Advanced Causal Inference Methods in Obstetrics and Gynecology: A Simulation Study on Preeclampsia Prevention

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality worldwide. Early preventive interventions, including low-dose aspirin therapy, reduce risk in high-risk pregnancies. Observational studies often face confounding, nonlinear covariate interactions, and heterogeneous treatment effects. We conducted a comprehensive simulation study comparing naive logistic regression (NL), propensity score weighting (PSW), regression adjustment (RA), doubly robust estimation (DR), targeted maximum likelihood estimation (TMLE), and causal forests (CF) in estimating aspirin effects on preeclampsia outcomes. Simulated cohorts reflected diverse confounding structures and treatment effect heterogeneity. Performance metrics included bias, standard deviation (SD), root mean squared error (RMSE), and coverage probability. TMLE and CF consistently yielded unbiased estimates and identified subgroup-specific effects, highlighting their potential in observational OB/GYN research.