Keith Barnatchez

Hi! I’m a fifth-year biostatistics PhD student at Harvard University, advised by Rachel Nethery and Giovanni Parmigiani. My research focuses on developing methods for causal inference when data is collected from multiple sources subject to different potential biases (e.g. hospital networks). My methods frequently use tools from machine learning and semiparametric theory to reduce reliance on restrictive modeling assumptions.

I am supported by a research assistantship with the Neurological Clinical Research Institute (NCRI) at Massachusetts General Hospital. At the NCRI, I work with Eric Macklin and Marie-Abele Bind on developing methods for external control trials in ALS research, as well as the development of statistical criteria for assessing potential surrogate outcomes of disease progression.

Before grad school, I did my undergrad at Colby College in Waterville, Maine. After graduating, I spent three years working as a research assistant at the Federal Reserve Bank of Boston.

You can contact me at keithbarnatchez@g.harvard.edu

News

• Our paper Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements was co-awarded best student poster at the 2025 New England Rare Disease Statistics (NERDS) Workshop! Our pre-print can be found on arXiv.

• Our manuscript Flexible and Efficient Estimation of Causal Effects with Error-Prone Exposures: A Control Variates Approach for Measurement Error was accepted to Biometrics! You can view our pre-print on arXiv.

• Our R package drcmd: Doubly-Robust Causal Inference with Missing Data is now available through GitHub. Please check back frequently, as the packge is being actively updated!