I'm a graduate student at Carnegie Mellon University working on a
PhD in Logic, Computation, and Methodology. This is an interdisciplinary program in CMU's philosophy department. Before coming to CMU, I did my undergraduate studies at Columbia University.
I study methods of causal inference: specifically, how researchers can use tools from statistics and machine learning to predict the outcomes of interventions or policies. Some of my research involves designing and implementing algorithms for learning causal structure (represented by causal graphical models or Bayes nets) from observational data. These can be used to estimate the consequences of manipulations, which might be social/economic policies, medical treatments, or some other changes to the system under study. I focus on applications of these methods in the social sciences (economics, sociology, political science, epidemiology) and in biomedicine. I also work in the foundations of statistics and probability, including decision theory. Some of this work has been done with the intent of making foundational issues salient in the context of experimental science, especially particle physics.
I am member of the algorithm development / data science research group in the NIH-funded Center for Causal Discovery.
Visit the research page for a look at some of my papers and presentations. The teaching page has syllabi for courses I have taught at CMU.
Contact:
malinsky {at} cmu {dot} edu
PhD in Logic, Computation, and Methodology. This is an interdisciplinary program in CMU's philosophy department. Before coming to CMU, I did my undergraduate studies at Columbia University.
I study methods of causal inference: specifically, how researchers can use tools from statistics and machine learning to predict the outcomes of interventions or policies. Some of my research involves designing and implementing algorithms for learning causal structure (represented by causal graphical models or Bayes nets) from observational data. These can be used to estimate the consequences of manipulations, which might be social/economic policies, medical treatments, or some other changes to the system under study. I focus on applications of these methods in the social sciences (economics, sociology, political science, epidemiology) and in biomedicine. I also work in the foundations of statistics and probability, including decision theory. Some of this work has been done with the intent of making foundational issues salient in the context of experimental science, especially particle physics.
I am member of the algorithm development / data science research group in the NIH-funded Center for Causal Discovery.
Visit the research page for a look at some of my papers and presentations. The teaching page has syllabi for courses I have taught at CMU.
Contact:
malinsky {at} cmu {dot} edu
