arXiv subjects

Ting-Hsuan Chang

Publications and source records attributed to Ting-Hsuan Chang.

6 recordsLinked to original sources

arXiv2026

Evaluating differential air quality impacts of prescribed fire and wildfire in the United States

Increasing wildfire activities in the United States have generated far-reaching effects on air pollution and human health. While prescribed fires are used as a preventive strategy to reduce wildfire risks, the health impacts of their smoke remain uncertain. Small-scale case studies indicate that prescribed fires generally produce lower pollutant emissions than uncontrolled wildfires, potentially benefiting air quality. However, the extent to which this translates into reduced population-level smoke exposure remains uncertain due to a lack of robust, large-scale quantitative evidence. Moreover, the factors driving differences in air quality impacts between prescribed fires and wildfires were underexplored. Using fire data in California, Florida, and Georgia (2006-2020) and a causal forest approach, we quantified and compared the impacts of prescribed fires and wildfires on air quality, assessed their differences in population-level smoke exposure, and identified key factors influencing their smoke exposure levels. On a per square-kilometer basis, prescribed fires resulted in over six times less population-level smoke exposure compared to wildfires. Additionally, prescribed fires were associated with lower smoke exposure in regions with higher relative humidity and precipitation. This work could enable the broad risk-benefit assessment to guide the judicious use of prescribed fires and optimize public health outcomes while mitigating wildfire threats.

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arXiv2026

Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding

Causal graphs may inform covariate adjustment for estimating causal effects and improve estimation efficiency by exploiting the graphical structure. In many applications, however, the target causal parameter may not be point-identified due to the presence of unmeasured confounding. Sensitivity analysis methods address this challenge by characterizing bounds on the causal parameter under varying assumptions about the magnitude or form of unmeasured confounding. We focus on semiparametric efficient estimation of causal effects in non-identifiable settings, assuming a known (or hypothesized) causal graph. We propose an influence function projection approach that exploits the conditional independence constraints implied by the graph to improve the efficiency of semiparametric estimators of upper and lower bounds on the average causal effect under a given sensitivity analysis model. Our approach applies across multiple sensitivity analysis frameworks and causal estimands, thereby connecting knowledge of graphical structure with the sensitivity analysis literature. We illustrate our approach through simulations and real data examples thought to be affected by unmeasured confounding, including the effect of labor training program on post-intervention earnings, and the effect of low ejection fraction on heart failure death.

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arXiv2026

Estimation Strategies for Causal Decomposition Analysis with Allowability Specifications

Causal decomposition analysis (CDA) is an approach for modeling the impact of hypothetical interventions to reduce disparities. It is useful for identifying foci that future interventions, including multilevel and multimodal interventions, could focus on to reduce disparities. Based within the potential outcomes framework, CDA has a causal interpretation when the identifying assumptions are met. CDA also allows an analyst to consider which covariates are allowable (i.e., fair) for defining the disparity in the outcome and in the point of intervention, so that its interpretation is also meaningful. While the incorporation of causal inference and allowability promotes robustness, transparency, and dialogue in disparities research, it can lead to challenges in estimation such as the need to correctly model densities. Also, how CDA differs from commonly used statistical decomposition estimators from the econometrics literature may not be clear, which may limit its uptake. To address these challenges, we provide a tour of estimation strategies for CDA, reviewing existing proposals and introducing novel estimators that overcome key estimation challenges. Among them we introduce what we call "bridging" estimators that avoid modeling any density, and sequential weighted regression estimators that are multiply robust. Additionally, we provide diagnostics to assess the quality of the nuisance density models and weighting functions they rely on. We formally establish the estimators' robustness to model mis-specification, demonstrate their performance through a simulation study based on real data, and apply them to study disparities in uncontrolled hypertension using electronic health records in a large healthcare system.

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arXiv2025

Post-selection inference for causal effects after causal discovery

Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation of causal effects (e.g., average treatment effects) when there is uncertainty about which covariates ought to be adjusted for, or which variables act as confounders versus mediators. However, naively using the data twice, for model selection and estimation, would lead to invalid confidence intervals. Moreover, if the selected graph is incorrect, the inferential claims may apply to a selected functional that is distinct from the actual causal effect. We propose an approach to post-selection inference that is based on a resampling and screening procedure, which essentially performs causal discovery multiple times with randomly varying intermediate test statistics. Then, an estimate of the target causal effect and corresponding confidence sets are constructed from a union of individual graph-based estimates and intervals. We show that this construction has asymptotically correct coverage for the true causal effect parameter. Importantly, the guarantee holds for a fixed population-level effect, not a data-dependent or selection-dependent quantity. Most of our exposition focuses on the PC-algorithm for learning directed acyclic graphs and the multivariate Gaussian case for simplicity, but the approach is general and modular, so it may be used with other conditional independence based discovery algorithms and distributional families.

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arXiv2023

Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

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arXiv2021

Global Trends and Predictors of Face Mask Usage During the COVID-19 Pandemic

Background: Guidelines and recommendations from public health authorities related to face masks have been essential in containing the COVID-19 pandemic. We assessed the prevalence and correlates of mask usage during the pandemic. Methods: We examined a total of 13,723,810 responses to a daily cross-sectional representative online survey in 38 countries who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last seven days. The outcome was individual face mask usage in public settings, and the predictors were country fixed effects, country-level mask policy stringency, calendar time, individual sociodemographic factors, and health prevention behaviors. Associations were modelled using survey-weighted multivariable logistic regression. Findings: Mask-wearing varied over time and across the 38 countries. While some countries consistently showed high prevalence throughout, in other countries mask usage increased gradually, and a few other countries remained at low prevalence. Controlling for time and country fixed effects, sociodemographic factors (older age, female gender, education, urbanicity) and stricter mask-related policies were significantly associated with higher mask usage in public settings, while social behaviors considered risky in the context of the pandemic (going out to large events, restaurants, shopping centers, and socializing outside of the household) were associated with lower mask use. Interpretation: The decision to wear a face mask in public settings is significantly associated with sociodemographic factors, risky social behaviors, and mask policies. This has important implications for health prevention policies and messaging, including the potential need for more targeted policy and messaging design.

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