Emil Pitkin

Andreas Buja, Lawrence D. Brown, Richard A. Berk, Edward I. George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao (2019), Models as Approximations I: Consequences Illustrated with Linear Regression, Statistical Science, 34 (4), pp. 523-544.

Richard A. Berk, Emil Pitkin, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2014), Covariance Adjustments for the Analysis of Randomized Field Experiments, Evaluation Review, 37 (3-4), pp. 170-196.

Abstract: It has become common practice to analyze randomized experiments using linear regression with covariates. Improved precision of treatment effect estimates is the usual motivation. In a series of important articles, David Freedman showed that this approach can be badly flawed. Recent work by Winston Lin offers partial remedies, but important problems remain.

Emil Pitkin, Richard A. Berk, Lawrence D. Brown, Andreas Buja, Edward I. George, Kai Zhang, Linda Zhao (Under Review), Improved Precision in Estimating Average Treatment Effects.

Richard A. Berk, Lawrence D. Brown, Andreas Buja, Edward I. George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao, “What You Can Learn From Wrong Causal Models”. In Handbook of Causal Analysis for Social Research, edited by Stephen Morgan, (: Springer Netherlands, 2013), pp. 403-424