title: Covariance adjustments for the analysis of randomized field experiments.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  In this article, we address those problems through a reformulation of the Neyman causal model. We provide a practical estimator and valid standard errors for the average treatment effect. Proper generalizations to well-defined populations can follow.
  
  
  CONCLUSIONS
  In most applications, the use of covariates to improve precision is not worth the trouble.
authors.affiliation: Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Berk
authors.forename: Emil
authors.initials: E
authors.lastname: Pitkin
authors.forename: Lawrence
authors.initials: L
authors.lastname: Brown
authors.forename: Andreas
authors.initials: A
authors.lastname: Buja
authors.forename: Edward
authors.initials: E
authors.lastname: George
authors.forename: Linda
authors.initials: L
authors.lastname: Zhao
mesh_terms: D015982:Bias; D015984:Causality; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012044:Regression Analysis
keywords: Neyman causal model.; covariate adjustments; randomized field experiments