Luke Miratrix is currently an Associate Professor at the Harvard Graduate School of Education. Prior to this, he spent three years as an assistant professor in the statistics department at Harvard. His primary interest, unsurprisingly given this, is to learn how to best use modern statistical methods in applied social science contexts. He directs the Miratrix C.A.R.E.S. lab, a group of students in statistics, education, and elsewhere dedicated to high quality causal inference research in the social sciences.

His primary focus is on how to best analyze data in a transparent manner that allows engagement from diverse stakeholders while also preserving rigor in said analyses. His most recent work in this vein is on best practices for impact evaluation in a variety of social science contexts.

Miratrix also interested in how to best use machine learning and other high-dimensional methodology for text analysis. The principle of transparency in this context means working evaluate how the results from statistical models connect to human measures of meaning.

While primarily focused on problems in education (ranging from evaluating early childhood impact evaluations to designing risk detection methods for community college), he has also worked on projects in elections and voting systems, media analysis, behavioral political science, the effectiveness of regulatory agencies such as OSHA, pre-trial risk assessment systems and criminal justice reform, and human-computer interactions.

He received his Doctorate in Statistics from University of California, Berkeley in Spring, 2012. His interest in Statistics came out of an interest in mathematics education which developed while being a high school teacher and tutor for 7 years. He also has a Masters in Computer Science from M.I.T., a Bachelors of Science in Computer Science from the California Institute of Technology, and a Bachelors of Arts in Mathematics from Reed College.

Some Selected Research Interests

• Applications in the social sciences with particular emphasis on political science, text data, and education.
• Causal inference (propensity scores, matching, regression discontinuity designs, instrumental variables when forced, and so forth).
• Principal Stratification (a method for causal analysis that incorporate post-treatment covariates)
• Criminal justice reform, in particular the evaluation of policy reforms relying in risk assessment methods.
• Assessing and characterizing variation in treatment effects (treatment heterogeneity).
• Analyzing data from randomized clinical trials, in particular multisite trials.
• High-dimensional and sparse-regression methods.
• Bayesian modeling (e.g., gaussian processes).
• Non-parametric analysis of randomized experiments.
• Random effect (multilevel) models.
• Text summarization and key-phrase extraction.

https://orcid.org/0000-0002-0078-1906