Resources

Prof. Luke Miratrix's CV

Here.

Random selection of favorite books and papers

The following books and papers are cornerstones of how we think about statistics (more or less). They are certainly worth being familiar with, along with all of our own work, of course.

Lin, W. (2013). Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique. The Annals of Applied Statistics, 7(1), 295–318.

An important investigation of how linear regression works from a randomization viewpoint. It also showcases the Neyman-Rubin causal model, and how finite-sample inference couples with survey sampling techniques.

Rosenbaum, P. R. (2009). Design of Observational Studies. New York: Springer Science & Business Media.

An important overview of causal inference, permutation testing, propensity scores, and matching. More importantly, this book provides much guidance on how to think about evidence in observational studies. See further review here.

Maxime Richard's award winning poster on geographic regression discontinuity designs.

Maxime Rischard's Poster

This work is part of a two-paper series looking at how spatial statistics can be coupled with RDD.  For our first paper, see A Nonparametric Bayesian Methodology for Regression Discontinuity Designs.  For the second, see A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?.