Seminar 2025-02-12: Chamika Porage

Topic Prognostic score methods for the estimation of the average causal effects

Abstract The prognostic score (PGS) is a function of observed covariates that summarizes covariates' association with potential responses. In the current study, we propose a full prognostic score (FPGS), an extension of the PGS that integrates individual prognostic scores to account for confounding adjustments in causal inference. Under effect modification, we demonstrate that FPGS meets the sufficiency condition for confounding adjustment, and implemented FPGS is sufficient for estimating the average causal effect. To estimate PGS and FPGS, we apply linear regression, random forest regression, XGBoost regression, and support vector machine regression. When determining the average treatment effect, we incorporate FPGS into semi-parametric estimators including regression imputation and targeted maximum likelihood estimation (TMLE). The finite sample properties of estimators are compared through three simulation studies. Based on the findings of FPGS estimators, the mean squared error of the linear regression imputation estimator and TMLE estimator comprised of linearly regressed PGS are smaller than the mean squared error of alternative estimators. In an empirical study, we analyze data from the National Health and Nutrition Examination Survey (NHANES, 2007-2008) to determine the effect of smoking on blood lead levels.