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Beijing Technology and Business University

Academic institutionasia · cn
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Research library67linked papers
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Selected work

Representative Papers

Quantifying Individual Risk for Binary Outcome

Feb 16, 2024

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.

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Functional BART with Shape Priors: A Bayesian Tree Approach to Constrained Functional Regression

Feb 24, 2025

This paper addresses nonparametric regression with functional responses and scalar predictors. We propose Functional BART (FBART), the first extension of the Bayesian Additive Regression Trees (BART) framework to functional-response settings, integrating B-spline basis expansions with Bayesian tree partitioning. We innovatively introduce embeddable shape-constrained priors—such as monotonicity and convexity—that rigorously enforce domain knowledge in posterior samples. Furthermore, we establish a novel posterior contraction rate theory adaptive to the unknown smoothness of response curves. FBART employs customized Bayesian backfitting and shape-constrained MCMC sampling. Extensive simulations and real-data applications demonstrate that FBART significantly outperforms existing methods in estimation accuracy and predictive performance, while offering strong interpretability, modeling flexibility, and rigorous theoretical guarantees.

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Position: A Potential Outcomes Perspective on Pearl's Causal Hierarchy

Jan 28, 2026

This study systematically reconstructs Pearl’s causal hierarchy from the potential outcomes framework, mapping causal estimands at each level to distinct features of the potential outcomes distribution and analyzing their identifiability conditions and strategies. By integrating the potential outcomes model, structural causal models, and formal classification methods, this work establishes—for the first time—a comprehensive taxonomy of Pearl’s causal hierarchy from the perspective of potential outcomes. The analysis not only clarifies the identification challenges inherent to estimands at each level but also deepens understanding of Level 3 (counterfactual) estimands. Furthermore, it reveals that higher-level estimands rely on stronger identification assumptions and correspond to richer information in the potential outcomes distribution, thereby offering a novel theoretical and practical perspective for causal inference.

1 citationsRead paper
Recent publications

Latest Papers

Selecting among Missingness Models for Sequential Outcomes with Nonignorable Nonresponse

Aug 09, 2026

This study addresses the nonignorable nonrandom missingness arising from self-censoring in longitudinal studies, particularly when later responses depend on prior observations. It introduces, for the first time, two classes of graphical-model-based missing mechanisms that enable identification of the full-data distribution under rank or completeness conditions. A two-stage Vuong-type selection procedure is proposed: first testing for observable distinguishability between candidate models, then selecting the preferred model based on Kullback–Leibler divergence to ensure asymptotic validity of Wald inference under the chosen model. The framework jointly guarantees model distinguishability and selection consistency, demonstrates robust performance in simulations, and is successfully applied to Job Corps data, yielding a principled choice of missing mechanism and reliable estimation of functional parameters.

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