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In an influential critique of empirical practice, Freedman (2008) showed that the linear regression estimator was biased for the analysis of randomized controlled trials under the randomization model.
An approximate distribution of estimates of variance components
Franklin E Satterthwaite · 1946
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Limit theorems for regressions with unequal and dependent errors
Friedhelm Eicker · 1967
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The behavior of maximum likelihood estimates under nonstandard condition
Peter J. Huber · 1967
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Wage discrimination: reduced form and structural estimates
Alan S Blinder · 1973
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Male-female wage differentials in urban labor markets
Ronald Oaxaca · 1973
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A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
Halbert White · 1980
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Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties
James G MacKinnon and Halbert White · 1985
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Formal modes of statistical inference for causal effects
Donald B. Rubin · 1990
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On the application of probability theory to agricultural experiments. essay on principles. section 9
Jerzy Splawa-Neyman, Dorota M Dabrowska, and TP Speed · 1990
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An introduction to the bootstrap
Robert J Tibshirani and Bradley Efron · 1993
Earlier work this paper cites.
Bias reduction in standard errors for linear regression with multi-stage samples
Robert M Bell and Daniel F McCaffrey · 2002
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Using randomization in development economics research: A toolkit
Esther Duflo, Rachel Glennerster, and Michael Kremer · 2007
Cited alongside, same era.
Mostly harmless econometrics
Joshua D Angrist and Jörn-Steffen Pischke · 2008
Cited alongside, same era.
on regression adjustments to experimental data
David A Freedman · 2008
Cited alongside, same era.
on regression adjustments in experiments with several treatments
David A Freedman · 2008
Cited alongside, same era.
Incentives and services for college achievement: Evidence from a randomized trial
Joshua Angrist, Daniel Lang, and Philip Oreopoulos · 2009
Cited alongside, same era.
Better late than nothing: Some comments on deaton (2009) and heckman and urzua (2009)
Guido W Imbens · 2010
Cited alongside, same era.
Agnostic notes on regression adjustments to experimental data: Reexamining freedman’s critique
Winston Lin · 2013
Later among the works it cites.
Essays on Causal Inference in Randomized Experiments
Winston Lin · 2013
Later among the works it cites.
Adjusting treatment effect estimates by post-stratification in randomized experiments
Luke W Miratrix, Jasjeet S Sekhon, and Bin Yu · 2013
Later among the works it cites.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
Later among the works it cites.
The econometrics of randomized experiments
Susan Athey and Guido W Imbens · 2017
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The practicalities of running randomized evaluations: Partnerships, measurement, ethics, and transparency
Rachel Glennerster · 2017
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Field experiments in labor economics
John A List and Imran Rasul · 2011
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Natural experiments in the social sciences: a design-based approach
Thad Dunning · 2012
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On equivalencies between design-based and regression-based variance estimators for randomized experiments
Cyrus Samii and Peter M Aronow · 2012
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A class of unbiased estimators of the average treatment effect in randomized experiments
Peter M Aronow and Joel A Middleton · 2013
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Later among the works it cites.
Understanding and misunderstanding randomized controlled trials
Angus Deaton and Nancy Cartwright · 2018
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The loop estimator: Adjusting for covariates in randomized experiments
Edward Wu and Johann A Gagnon-Bartsch · 2018
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clubSandwich: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
James Pustejovsky · 2021
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Effect size is significantly more important than statistical significance., 2021
Ben Recht · 2021
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