🤖 AI Summary
This paper addresses the challenge of quantifying individual-level treatment risk in binary-outcome settings. We propose the Fraction of Negative Average (FNA) metric—the proportion of individuals whose outcomes deteriorate upon treatment—thereby complementing the Conditional Average Treatment Effect (CATE), which captures only subgroup-level averages and obscures individual harm. Under the ignorability assumption, we introduce the Pearson correlation coefficient between potential outcomes as a sensitivity parameter and derive tight, feasible theoretical bounds for FNA—substantially improving upon the classical Fréchet–Hoeffding bounds. We establish an analytical relationship among FNA, CATE, and the correlation coefficient, revealing the counterintuitive phenomenon that positive CATE can coexist with substantial individual harm. We further propose principled guidelines for selecting plausible correlation ranges and develop a nonparametric estimator for FNA that is consistent and asymptotically normal.
📝 Abstract
Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual-level risks, potentially leading to misleading results. This article fills this gap by examining individual risk for binary outcomes, specifically focusing on the fraction negatively affected (FNA), a metric that quantifies the percentage of individuals experiencing worse outcomes under treatment compared with control. Even under the strong ignorability assumption, FNA is still unidentifiable, and the existing Frechet-Hoeffding bounds are usually too wide and attainable only under extreme data-generating processes. By invoking mild conditions on the value range of the Pearson correlation coefficient between potential outcomes, we obtain improved bounds compared with previous studies. We show that paradoxically, even with a positive CATE, the lower bound on FNA can be positive, i.e., in the best-case scenario many units will be harmed if they receive treatment. Additionally, we establish a nonparametric sensitivity analysis framework for FNA using the Pearson correlation coefficient as the sensitivity parameter, thereby exploring the relationships among the correlation coefficient, FNA, and CATE. We also propose a method for selecting the range of correlation coefficients. Furthermore, we propose nonparametric estimators for the refined FNA bounds and prove their consistency and asymptotic normality.