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Ph.D., University of California, Berkeley, (2012)
Luke Miratrix was most recently an assistant professor of Statistics in the Faculty of Arts and Sciences at Harvard University. His primary research focus is on causality with a focus on developing methodology to assess and characterize treatment effect heterogeneity in randomized clinical trials and observational studies. He also has conducted statistical evaluations of large-scale cluster-randomized trials of education interventions. Other research interests include data mining using high-dimensional and sparse (regularized) methods, with a focus on text summarization in contexts such as newspaper corpora, legal decisions, and databases of free-text reports. Miratrix received his doctorate in Statistics from University of California, Berkeley in spring 2012 after switching to that field in 2009 from SESAME, a doctorate program in Mathematics and Science education also at Berkeley. He also has an M.S. in Computer Science from M.I.T., a B.S. in Computer Science from the California Institute of Technology, and a B.A. in Mathematics from Reed College. Between graduate careers, he was a high school teacher and tutor for seven years.
Miratrix’s paper, "Worth Weighting? How to think about and use weights in survey experiments", co-authored with Jasjeet Sekhon, Alexander Theodoridis, and Luis Campos, won the 2019 edition of the Warren Miller Prize, awarded by the Society of Political Methodology for the best work appearing in the journal Political Analysis in the preceding year. Miratrix was also awarded the Society for Research on Educational Effectiveness (SREE) Early Career Award for 2019.
Click here to see a full list of Luke Miratrix's courses.
In large-scale randomized trials that use text as an outcome, researchers often have the capacity to code only a portion of all available text, as it is a labor-intensive, often slow, process. The end result represents a massive simplification of the data; written language encodes information far more rich than what can feasibly be extracted by a human rater. We seek to facilitate and enhance human scoring efforts in order to radically improve and expand the use of text dataa product that is tied to essential skills, and that also can capture deep understanding of contentas an outcome in education evaluations.