Dan Thal

Dan Thal is a senior statistician who specializes in Bayesian methods for causal inference. He has worked on a wide variety of evaluations, using the power of Bayesian inference to answer clients’ pressing questions. He has also developed extensions to cutting-edge Bayesian machine learning techniques to better suit clients’ evaluation settings.

He has over a decade of experience leading evaluations, serving as the task lead for the analysis of the America’s Promise Job-Driven Training Grants Evaluation, Understanding Economic Risk for Low-Income Families, and the evaluation of the Transforming Clinical Practices Initiative. He also led the analysis of “exemplar” practices for the Primary Care First evaluation, developing extensions to Bayesian causal forests to identify primary care practices which improved the most relative to expectations. He is also an expert in the field of Bayesian meta-analysis, having designed and implemented meta-analyses of the What Works Clearinghouse, the Pathways to Work Evidence Clearinghouse, the Teen Pregnancy Prevention Evidence Review, and of a body of studies focusing on Medicare primary care.