https://abhijainstats.github.io/research/Recent content in Research on Abhi JainHugo -- gohugo.ioenSat, 01 Aug 2026 00:00:00 +0000https://abhijainstats.github.io/research/jtbcf/Sat, 01 Aug 2026 00:00:00 +0000https://abhijainstats.github.io/research/jtbcf/Estimating the causal effect of community exposures, such as county or ZIP Code access to healthy food on health outcomes, is methodologically challenging because of 1) interference – the exposure in one community can affect health outcomes in nearby communities and 2) heterogeneous effects – the magnitude and direction of the effect of the exposure can vary by community characteristics such as socioeconomic status. We propose a new method, joint treatment Bayesian Causal Forest (BCF), to estimate the direct and indirect effects of community-level exposures.https://abhijainstats.github.io/research/wbi-double-beta/Sun, 01 Mar 2026 00:00:00 +0000https://abhijainstats.github.io/research/wbi-double-beta/Health and well-being indices are widely used to assess population health outcomes and inform policy decisions. Individual-level assessment of well-being can be used to develop community-level indices that measure wellness for different geographical units. While many existing indices operate at coarse geographic levels such as counties or states, finer spatial resolution can offer more actionable insights. We present a novel Bayesian double generalized beta regression framework to model a bounded individual-level well-being index (WBI) using annual survey data collected from 2021 to 2023 in Massachusetts.https://abhijainstats.github.io/research/wellbeing-zcta/Wed, 03 Apr 2024 00:00:00 +0000https://abhijainstats.github.io/research/wellbeing-zcta/Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling.