I am an Assistant Professor of Statistics in the Mathematical Sciences Department of Claremont McKenna College. My research focuses on causal inference, with applications to problems in political science and public health. I am particularly motivated by problems involving hybridizing observational and experimental data to better estimate causal effects; and by applications in modern electioneering, such as ecological inference and prediction calibration.

Prior to CMC, I was a Postdoctoral Fellow at the Harvard Data Science Initiative, where I was affiliated with the Department of Statistics and the Institute for Quantitative Social Sciences. I was advised by Kosuke Imai and Luke Miratrix.

I earned my doctorate from the Statistics Department at Stanford University in 2020, advised by Art Owen and Mike Baiocchi. My dissertation focused on combining data from disparate sources to make robust causal conclusions.

After completing my PhD and before starting my post doc, I worked as a Data Scientist for the Biden for President campaign, where I built and maintained the models to predict individual-level voter turnout in battleground states. I have also provided pro bono analytics services to several progressive organizations.

You can find me on Twitter, LinkedIn, or Google Scholar. You can also email me at erosenman[at]cmc.edu. Further information can be found on my CV.