Is regression adjustment supported by the Neyman model for causal inference

By Peter Z. Schochet. 2007

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Subfields: Education, Gender Studies, Statistics and Probability

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, methodology): “ large sample approximations. This is particularly true once one uses nonlinear methods. See for additional discussions Lesaﬀre and Senn (2003), Samii and Aronow (2012), Rosenbaum (2002), Lin (2013), Schochet (2010), Young (2016), Senn (1994). Ultimately we recommend that researchers wishing to use ”
A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments (2013, methodology): “[8] is not invariant, let Y 1i be a linear transformation of the treatment outcome for the ith person such that”
Statistical Power for Regression Discontinuity Designs in Education Evaluations (2009, methodology): “OLS methods yield consistent estimates of a1 in Equation 5 because T i and Scorei are asymptotically uncorrelated because of RA (Schochet, 2007; Yang & Tsiatis, 2001).”
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique (2012, methodology): “In Stonehouse and Forrester’s (1998) simulations, Welch’s t-test was not robust to extreme skewness in the smaller group when that group’s sample size was 30 or smaller. That does not appear to be a serious issue in this example, however. The distribution of men’s first-year GPA in the services-and-”
Machine Learning and Causal Inference : A Modular Approach to Assessing the Effects of the London Bombings of 2005 (2015, background): “And posthoc adjustments for multiple testing require knowledge of the amount of and pattern of multiple testing (which may not be available if the hunt was not disciplined) (2) Concern and Controversy about Test Validity if the experiment did not randomly assign treatment and control to two equal…”
On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from over 40,000 Close Races (2014, unknown): “22 See Rubin (1973, 1979, 2009), Schochet (2010), and Miratrix, Sekhon, and Yu (2013) for discussions of when and how valid inferences can be drawn from imbalanced experimental data.”
Finite Population Causal Standard Errors (2014, methodology): “More recent studies analyze regression methods with additional covariates under the randomization distribution in randomized experiments, e.g., Freedman (2008ab), Lin (2013), and Schochet (2010).”
A paradox from randomization-based causal inference (2014, methodology): “Schochet (2010), Samii and Aronow (2012), Lin (2013) and Imbens and Rubin (2015) pointed out that we can solve this problem by using the Huber– White heteroskedasticity-robust variance estimator (Huber, 1967; White, 1980), and the corresponding Wald test is asymptotically the same as Neyman’s test.”
The Generalized Oaxaca-Blinder Estimator (2020, background): “Indeed, before Lin’s work, it was widely believed that regression adjustment to experimental data could not be justified on the basis of randomization alone (Freedman 2008a,b,c; Schochet 2010).”
Using an Experimental Evaluation of Charter Schools to Test Whether Nonexperimental Comparison Group Methods Can Replicate Experimental Impact Estimates (2012, methodology): “However, regression adjustment can improve the precision of the impact estimate (Schochet 2007).”