Colloquium November 18 - Daniel Malinsky, Columbia University

Picture of Daniel MalinskyComputer Science Colloquium

Friday, November 18

2:35pm in Wege Auditorium


Identifying Causal Determinants of Clinical Outcomes from Electronic Health Records Using Graphical Structure Learning: Challenges and Opportunities in Causal Discovery

Many goals within causal inference, including estimating average treatment effects and understanding path-specific mechanisms, depend on knowing the qualitative causal structure underlying a domain. In this work we apply methods for graphical causal discovery (specifically the FCI algorithm) to observational data in the form of electronic health records (EHR) from Johns Hopkins Hospital. Our goal is to understand the causal determinants of postoperative length of stay for patients undergoing cardiac surgery procedures, in order to inform possible interventions that support faster patient recovery. We discuss the challenges in applying causal discovery methods to electronic health records and opportunities for future work.

Daniel Malinsky’s methodological 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 graphical structure learning (a.k.a. causal discovery or causal model selection), semiparametric inference, time series analysis, and missing data. Application areas of particular interest include environmental determinants of health and health disparities. Dr. Malinsky also studies algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, Dr. Malinsky has interests in the philosophy of science and the foundations of statistics. Before joining Columbia University, Dr. Malinsky was a postdoctoral fellow at Johns Hopkins University and he earned his PhD at Carnegie Mellon University.