Events

In many scientific applications, uncertainty of estimates from an upstream analysis needs to be propagated into a downstream analysis without feedback. Cutting feedback, or cut-Bayes, achieves this by constructing a cut-posterior distribution that prevents backward information flow. However, sampling-based implementations of cutting feedback, like nested MCMC, are computationally intensive, while existing variational inference (VI) approaches require two approximations and access to upstream data. We propose NeVI-Cut, a neural network-based variational inference method for cutting feedback. NeVI-Cut directly uses upstream samples without requiring access to upstream data, avoiding extra approximation error. We employ normalizing flows, neural network-based generative models, to flexibly model the downstream conditional distribution. We provide theoretical guarantees for NeVI-Cut to approximate any cut-posterior. A triply stochastic algorithm implements the method. Simulation studies and two real-world analysis show that NeVI-Cut achieves substantial computational gains over sampling-based cutting feedback methods and is more accurate than parametric variational cut approaches.

Environmental policies often aim to mitigate contaminant harm by adhering to a regulatory threshold. A key example is managing nitrate—a prevalent contaminant from agricultural fertilizers—in groundwater. While increasing drinking water well depth is an effective intervention, it incurs significant costs. Policymakers must therefore determine the minimum well depth required to meet the regulatory threshold. In this paper, we propose a policy learning framework to identify the minimum treatment level needed at a given location to meet the threshold. A key feature of our method is accounting for the spatial dependence of contaminants. We estimate the optimal policy by empirical risk minimization with a novel, nonparametric, doubly robust loss function and provide a cross-fitting SVM estimator. We then characterize the statistical properties of the estimated policy in terms of the excess risk bound. Our policy outperforms competing methods, including non-spatial policy learning and indirect methods, in our simulation studies. Finally, we apply our method to design a well replacement policy for Wisconsin, aiming to decrease the nitrate level in drinking water to the 10 ppm regulatory threshold.

Principal stratification provides a causal inference framework for investigating treatment effects in the presence of a post-treatment variable. Principal strata play a key role in characterizing the treatment effect by identifying groups of units with the same or similar values for the potential post-treatment variable under both treatment levels. The literature has focused mainly on binary post-treatment variables, while few papers considered continuous post-treatment variables. In the presence of a continuous post-treatment, a challenge is how to identify and characterize meaningful coarsening of the latent principal strata that lead to interpretable principal causal effects. We introduce the confounders-aware shared-atom Bayesian mixture, a novel approach for principal stratification with binary treatment and continuous post-treatment variables. Our method leverages Bayesian nonparametric priors with an innovative hierarchical structure for the potential post-treatment variable that overcomes some of the limitations of previous works. Specifically, the novel features of our method allow for (i) identifying coarsened principal strata through a data-adaptive approach and (ii) providing a comprehensive quantification of the uncertainty surrounding stratum membership. We illustrate the proposed methodology through two environmental applications. In the first case study, we estimate the causal effects of U.S. national air quality regulations on ambient pollution concentrations and associated health outcomes. In the second, we examine the causal pathway linking exposure to air pollution and socio-economic outcomes, specifically intergenerational social mobility, while formally accounting for the role of educational attainment as a post-treatment variable.

Mathematical modeling of infectious diseases plays an important role in the development and evaluation of intervention plans. These plans, such as the development of vaccines, are usually pathogen-specific, but laboratory confirmation of all pathogen-specific infections is rarely available. If an epidemic is a consequence of co-circulation of several pathogens, it is desirable to jointly model these pathogens in order to study the transmissibility of the disease to help inform public health policy.

A major challenge in utilizing laboratory test data is that it is not available for every infected person. Appropriate imputation of the missing pathogen information often requires a prohibitive amount of computation. To address it, we extend our earlier hierarchical Bayesian multi-pathogen framework that uses a latent process to link the disease counts and the lab test data. Under the proposed model, imputation of the unknown pathogen-specific cases can be effectively avoided by exploiting the relationship between multinomial and Poisson distributions. A variable selection prior is used to identify the risk factors and their proper functional form respecting the linear-nonlinear hierarchy. The efficiency gains of the proposed model and the performance of the selection priors are examined through simulation studies and on a real data case study from hand, foot and mouth disease (HFMD) in China.

Verbal autopsy (VA) algorithms are routinely employed in low- and middle-income countries to determine individual causes of death (COD). The CODs are then aggregated to estimate population-level cause-specific mortality fractions (CSMFs) essential for public health policymaking. However, VA algorithms often misclassify COD, introducing bias in CSMF estimates. A recent method, VA-calibration, addresses this bias by utilizing a VA misclassification matrix derived from limited labeled COD data collected in the CHAMPS project. Due to the limited labeled samples, the data are pooled across countries to improve estimation precision, thereby implicitly assuming homogeneity in misclassification rates across countries. In this presentation, I will highlight substantial cross-country heterogeneity in VA misclassification, challenging this homogeneity assumption and revealing its impact on VA-calibration’s efficacy. To address this, I will propose a comprehensive country-specific VA misclassification matrix modeling framework in data-scarce settings. The framework introduces a novel base model that parsimoniously characterizes the misclassification matrix through two latent mechanisms: intrinsic accuracy and systematic preference. We theoretically prove that these mechanisms are identifiable from the data and manifest as a form of invariance in misclassification odds, a pattern evident in the CHAMPS data. Building on this, the framework then incorporates cross-country heterogeneity through interpretable effect sizes and uses shrinkage priors to balance the bias-variance tradeoff in misclassification matrix estimation. This effort broadens VA-calibration’s applicability and strengthens ongoing efforts of using VA for mortality surveillance. I will illustrate this through simulations and applications to mortality surveillance projects, such as COMSA in Mozambique and CA CODE.