https://abhijainstats.github.io/Recent content 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/software/bayesbadger/Thu, 01 Jan 2026 00:00:00 +0000https://abhijainstats.github.io/software/bayesbadger/BayesBadger is an R package for a Bayesian BetA Double GEneralized Regression with mean modeled using individual-level covariates and precision modeled through cluster-level covariates. The model also allows for spatial smoothing through a graph Laplacian prior. A vignette with instructions on how to use the package can be found here and source code can be found on the Github repository.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.https://abhijainstats.github.io/contact/Mon, 01 Jan 0001 00:00:00 +0000https://abhijainstats.github.io/contact/ jaina22 at bu dot edu Crosstown Center (CT-346B), 801 Massachusetts Ave, Boston, MA 02118https://abhijainstats.github.io/contributors/Mon, 01 Jan 0001 00:00:00 +0000https://abhijainstats.github.io/contributors/Thank you to all the folks who have contributed both technical and creative skills to this project: Desirée De Leon 🦒 (designed 5 of the custom color themes, made illustrations for the workshop, and provided general aesthetic feedback along the way) Garrick Aden-Buie 🧙‍♀️ (debugged headroom.js and lent his panelset.js code to the theme) Allison Horst 🐕 (awesome illustrations of campfires, seedlings, and evergreens, as well as my R Markdown hedgehog mascot 🦔)https://abhijainstats.github.io/license/Mon, 01 Jan 0001 00:00:00 +0000https://abhijainstats.github.io/license/My blog posts are released under a Creative Commons Attribution-ShareAlike 4.0 International License.https://abhijainstats.github.io/teaching/Mon, 01 Jan 0001 00:00:00 +0000https://abhijainstats.github.io/teaching/Teaching Fellow Boston University School of Public Health — Department of Biostatistics SPH BS 880 — Biostatistics Capstone: Design and Analysis of Investigations (Fall 2026) SPH BS 755 — Theory of Linear Models in Biostatistics (Fall 2026) SPH BS 800 — Accelerated Statistical Training (Summer 2026) SPH BS 401S — Survey in Biostatistical Methods (Summer 2026) Teaching Assistant Boston University School of Public Health — Department of Biostatistics SPH BS 849 — Bayesian Modeling for Biomedical Research & Public Health (Spring 2026) SPH BS 807 — Applied Causal Inference in Health Research (Fall 2024) Wake Forest University — Department of Mathematics and Statistics