Chanmin Kim

  • Citations Per Year
Learn More
We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest(More)
INTRODUCTION The regulatory and policy environment surrounding air quality management warrants new types of epidemiological evidence. Whereas air pollution epidemiology has typically informed previous policies with estimates of exposure-response relationships between pollution and health outcomes, new types of evidence can inform current debates about the(More)
Mediators: Relating Principal Stratification and Causal Mediation in the Analysis of Power Plant Emission Controls” by Chanmin Kim1, Michael Daniels2, Joseph Hogan3, Christine Choirat1, and Corwin Zigler1 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 2Department of Statistics & Data Science and Department of(More)
OBJECTIVE To evaluate the effects and costs of three doses of behavioral weight-loss treatment delivered via Cooperative Extension Offices in rural communities. METHODS Obese adults (N = 612) were randomly assigned to low, moderate, or high doses of behavioral treatment (i.e., 16, 32, or 48 sessions over two years) or to a control condition that received(More)
We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1(More)
Clinical researches usually collected numerous intermediate variables besides treatment and outcome. These variables are often incorrectly treated as confounding factors and are thus controlled using a variety of multivariable regression models depending on the types of outcome variable. However, these methods fail to disentangle underlying mediating(More)
  • 1