Luke Miratrix

Luke Miratrix serves as Assistant Professor at Harvard Graduate School of Education and affiliate faculty in Harvard Department of Statistics. His methodological expertise centers on causal inference applications in educational research, particularly treatment effect heterogeneity and cluster-randomized trial evaluation.

His academic background includes a Doctorate in Statistics from University of California, Berkeley (2012), Master of Science in Computer Science from M.I.T., Bachelor of Science in Computer Science from California Institute of Technology, and Bachelor of Arts in Mathematics from Reed College. Prior to academia, he spent seven years as a high school teacher and tutor.

Miratrix's research prioritizes minimal-assumption statistical approaches to validate data-driven arguments. Key interests include developing methods for characterizing variation in treatment impacts, analyzing post-treatment subgroups, and applying high-dimensional techniques to text summarization in legal, journalistic, and educational contexts. His work consistently bridges theoretical statistics with practical implementation challenges in real-world settings.

Analysis of his recent publications (2023-2025) reveals three dominant trends: advancement of matching methodologies (e.g., synthetic controls, caliper matching), refinement of heterogeneous treatment effect estimation across multisite trials, and integration of machine learning with human coding for efficient text-based inference in educational assessments. These efforts demonstrate increasing focus on scalable, accessible tools for applied researchers.

He contributes to methodological infrastructure through the CARES Lab and software packages like 'matchMulti' and 'textreg', providing practical implementation guides for complex statistical techniques. His work emphasizes translating advanced causal inference methods into usable frameworks for education researchers and policymakers.