Direct Bias-Correction Term Estimation for Propensity Scores and Average Treatment Effect Estimation (2509.22122v1)

Abstract: This study considers the estimation of the average treatment effect (ATE). For ATE estimation, we estimate the propensity score through direct bias-correction term estimation. Let ${(X_i, D_i, Y_i)}_{i=1}^{n}$ be the observations, where $X_i \in \mathbb{R}^p$ denotes $p$-dimensional covariates, $D_i \in {0, 1}$ denotes a binary treatment assignment indicator, and $Y_i \in \mathbb{R}$ is an outcome. In ATE estimation, the bias-correction term $h_0(X_i, D_i) = \frac{1[D_i = 1]}{e_0(X_i)} - \frac{1[D_i = 0]}{1 - e_0(X_i)}$ plays an important role, where $e_0(X_i)$ is the propensity score, the probability of being assigned treatment $1$. In this study, we propose estimating $h_0$ (or equivalently the propensity score $e_0$) by directly minimizing the prediction error of $h_0$. Since the bias-correction term $h_0$ is essential for ATE estimation, this direct approach is expected to improve estimation accuracy for the ATE. For example, existing studies often employ maximum likelihood or covariate balancing to estimate $e_0$, but these approaches may not be optimal for accurately estimating $h_0$ or the ATE. We present a general framework for this direct bias-correction term estimation approach from the perspective of Bregman divergence minimization and conduct simulation studies to evaluate the effectiveness of the proposed method.