I'm an Assistant Professor of Biostatistics in the Mailman School of Public Health at Columbia University. Currently, I'm a visitor at UC Berkeley's Simons Institute.
My research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. Current research topics include structure learning (a.k.a. causal discovery or causal model selection), semiparametric inference, time series analysis, and missing data. I also work on algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, I have interests in the philosophy of science and the foundations of statistics.
Previously, I was a Postdoctoral Fellow at Johns Hopkins University. I completed my PhD at Carnegie Mellon University in 2017 and earned my BA from Columbia University in 2011.
Visit the research page for a look at some of my papers and presentations. My CV is available here.
Contact:
d.malinsky {at} columbia.edu
My research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. Current research topics include structure learning (a.k.a. causal discovery or causal model selection), semiparametric inference, time series analysis, and missing data. I also work on algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, I have interests in the philosophy of science and the foundations of statistics.
Previously, I was a Postdoctoral Fellow at Johns Hopkins University. I completed my PhD at Carnegie Mellon University in 2017 and earned my BA from Columbia University in 2011.
Visit the research page for a look at some of my papers and presentations. My CV is available here.
Contact:
d.malinsky {at} columbia.edu