arXiv subjects

Zhuochao Huang

Publications and source records attributed to Zhuochao Huang.

5 recordsLinked to original sources

arXiv2026

Win-Ratio Regression for Prioritized Composite Outcomes in Observational Studies: Doubly Robust and Efficient Estimation with Future-Score Correction

Prioritized pairwise outcomes are useful when clinical events follow a natural hierarchy, but censoring before pair resolution complicates estimation. We develop a win-ratio regression framework for this setting by defining a complete-data target over follow-up and deriving an estimating equation for the observed data. The central idea is future-score correction (FC): when censoring prevents later pairwise comparisons from being observed, the method replaces the remaining score with its conditional expectation given the observed history. This correction recovers pairwise information beyond that provided by inverse censoring weights alone. Additionally, we incorporate treatment weighting and baseline outcome augmentation to address baseline confounding. Together, these components yield double robustness for treatment assignment and censoring. Inference is obtained from U-statistic theory. Under standard regularity conditions, the AIPW-FC estimator is asymptotically normal and efficient when all nuisance functions are correctly specified. Simulations with 30%, 50%, and 65% censoring show that efficiency gains from future-score correction increase with the censoring rate, with relative efficiency reaching 1.50 under 65% censoring and near-nominal coverage for AIPW-FC. An application to OneFlorida electronic health record data illustrates the method for a composite outcome that prioritizes death over hospitalization.

stat.ME↗

arXiv2026

A Stable Mineral Fingerprint in the Fading Warm Debris Disk around HD 15407A

Extreme debris disks provide time-domain probes of rocky planet assembly after giant impacts. We compare Spitzer and JWST spectra of the warm debris disk around HD 15407A, obtained 14.95 yr apart. Across 6-25$μ$m, the stellar-subtracted mid-infrared excess declined by $14.0\pm1.2$%, while the mineral fingerprint is nearly unchanged. Within the adopted common reduced mineral basis, Mg-rich pyroxene dominates the normalized fitted mineral weights at about 65%, while silica and forsterite contribute about 22-23% and 12-13%, respectively. In both epochs, about 93-94% of the fitted mineral weight is carried by 5.0$μ$m grains. The fitted optically thin surface-component amplitude drops by 32%, consistent with reduced mineral-emitting flux from the disk surface rather than the appearance of a new dust composition. A simple collisional-cascade guide fitted to the IRAS-to-JWST multi-epoch ratios gives $t_c=87^{+17}_{-12}$ yr, for which the Spitzer-normalized dust-excess ratio reaches 0.5 after one $t_c$. HD 15407A is consistent with an evolved post-impact reservoir with limited recent supply of very small grains: its rocky mineral fingerprint has remained stable since at least 2008, while its warm mineral-emitting flux is gradually fading over several hundred years.

astro-ph.EP↗

arXiv2026

Multivariate incremental effects for continuous treatments: Studying the health effects of environmental mixtures

Evaluating the causal health effects of multivariate, continuous exposures, such as air pollution mixtures, is a critical public health challenge. A primary obstacle is the frequent violation of the positivity assumption, which renders the effects of standard deterministic interventions unidentified or heavily reliant on unreliable model extrapolation. In this paper, we develop a novel causal inference framework to address this challenge. We extend exponential tilting to multivariate exposures and address the critical question of how to compare different intervention directions fairly. This establishes a systematic framework for defining and evaluating various policy-relevant causal estimands, allowing researchers to address diverse scientific questions. We develop numerous methodological advancements, including efficient one-step estimation strategies, a Riemannian BFGS algorithm to solve a constrained manifold optimization problem, semiparametric efficiency bounds for causal estimands, minimax rates for estimators, and establishing asymptotic normality. We demonstrate our framework's utility by applying it to a nationwide environmental health dataset to identify the optimal strategy for reducing adverse health outcomes associated with a PM$_{2.5}$ chemical mixture.

stat.ME↗

arXiv2025

Why Is the Double-Robust Estimator for Causal Inference Not Doubly Robust for Variance Estimation?

Doubly robust estimators (DRE) are widely used in causal inference because they yield consistent estimators of average causal effect when at least one of the nuisance models, the propensity for treatment (exposure) or the outcome regression, is correct. However, double robustness does not extend to variance estimation; the influence-function (IF)-based variance estimator is consistent only when both nuisance parameters are correct. This raises concerns about applying DRE in practice, where model misspecification is inevitable. The recent paper by Shook-Sa et al. (2025, Biometrics, 81(2), ujaf054) demonstrated through Monte Carlo simulations that the IF-based variance estimator is biased. However, the paper's findings are empirical. The key question remains: why does the variance estimator fail in double robustness, and under what conditions do alternatives succeed, such as the ones demonstrated in Shook-Sa et al. 2025. In this paper, we develop a formal theory to clarify the efficiency properties of DRE that underlie these empirical findings. We also introduce alternative strategies, including a mixture-based framework underlying the sample-splitting and crossfitting approaches, to achieve valid inference with misspecified nuisance parameters. Our considerations are illustrated with simulation and real study data.

stat.ME↗

arXiv2025

Causal inference and racial bias in policing: New estimands and the importance of mobility data

Studying racial bias in policing is a critically important problem, but one that comes with a number of inherent difficulties due to the nature of the available data. In this manuscript we tackle multiple key issues in the causal analysis of racial bias in policing. First, we formalize race and place policing, the idea that individuals of one race are policed differently when they are in neighborhoods primarily made up of individuals of other races. We develop an estimand to study this question rigorously, show the assumptions necessary for causal identification, and develop sensitivity analyses to assess robustness to violations of key assumptions. Additionally, we investigate difficulties with existing estimands targeting racial bias in policing. We show for these estimands, and the estimands developed in this manuscript, that estimation can benefit from incorporating mobility data into analyses. We apply these ideas to a study in New York City, where we find a large amount of racial bias, as well as race and place policing, and that these findings are robust to large violations of untestable assumptions. We additionally show that mobility data can make substantial impacts on the resulting estimates, suggesting it should be used whenever possible in subsequent studies.

stat.AP↗