Dr. Daniel Malinsky

Biography

I am an Assistant Professor of Biostatistics at the Mailman School of Public Health at Columbia University. My research primarily focuses on causal inference, developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. I adopt an informed graphical models perspective, utilizing approaches such as Directed Acyclic Graphs (DAGs) and Bayesian networks. Current research topics include structure learning, also known as causal discovery, semiparametric inference, time series analysis, and handling missing data. I am also actively engaged in the area of algorithmic fairness, working to understand and counteract biases introduced by data science tools in socially impactful settings. Additionally, I have interests in the philosophy of science and the foundational aspects of statistics.

Research Interests