EconBase
← All papers

Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials

Haoge Chang, Joel Middleton, P. M. Aronow

arXiv 16 Oct 2021 · Statistics — Methodology · publishedEconometrica (2024) · 6 citations (OpenAlex)

arXiv:2110.08425 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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. Under Freedman's assumptions, we derive exact closed-form bias corrections for the linear regression estimator with and without treatment-by-covariate interactions. We show that the limiting distribution of the bias corrected estimator is identical to the uncorrected estimator, implying that the asymptotic gains from adjustment can be attained without introducing any risk of bias. Taken together with results from Lin (2013), our results show that Freedman's theoretical arguments against the use of regression adjustment can be completely resolved with minor modifications to practice.

Citation extraction

31
references
70
in-text mentions
31
distinct cited
2
self-citations
5,262
main-text words

appendix boundary found by appendix_command · 51% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Winston Lin (2013) Agnostic notes on regression adjustments to experimental data: Reexamining freedman’s critique1.000115100%
2David A Freedman (2008) aon regression adjustments to experimental data1.00064100%
3David A Freedman (2008) bon regression adjustments in experiments with several treatments0.8434475%
4Guido W Imbens (2010) Better late than nothing: Some comments on deaton (2009) and heckman and urzua (2009)0.64422100%
5Guido W Imbens and Donald B Rubin (2015) Causal inference in statistics, social, and biomedical sciences0.64422100%
6Jerzy Splawa-Neyman, Dorota M Dabrowska, and TP Speed (1923) On the application of probability theory to agricultural experiments. essay on principles. section 90.64422100%
7James Pustejovsky (2021) clubSandwich: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections, 20210.53613223%
8Joshua Angrist, Daniel Lang, and Philip Oreopoulos (2009) Incentives and services for college achievement: Evidence from a randomized trial0.51121100%
9Peter M Aronow and Joel A Middleton (2013) A class of unbiased estimators of the average treatment effect in randomized experiments self0.51121100%
10Edward Wu and Johann A Gagnon-Bartsch (2018) The loop estimator: Adjusting for covariates in randomized experiments0.51121100%

Showing the top 10 of 31 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Covariate Adjustment in Randomized Experiments Motivated by Higher-Order Influence Functions0.87452
2Unbiased Regression-Adjusted Estimation of Average Treatment Effects in Randomized Controlled Trials0.64422
3Assumption-lean covariate adjustment under covariate adaptive randomization when $p = o (n)$0.64422
4Regression adjustment in completely randomized experiments with many covariates0.51121
5Robust and Efficient Estimation of Potential Outcome Means Under Random Assignment0.40511
6Flexible Covariate Adjustments in Regression Discontinuity DesignsFirst version: July 16, 2021. This version: . We thank Sebastian Calonico, Michal Kolesár, Thomas Lemieux, Jonathan Roth, Vira Semenova, Stefan Wager, Daniel Wilhelm, Andrei Zeleneev, and numerous conference and seminar participants for helpful comments and suggestions. We thank Tobias Grobölting and Merve Ögretmek for excellent research assistance. The authors gratefully acknowledge financial support by the European Research Council (ERC) through grant SH1-77202. The second author also gratefully acknowledges support from the European Research Council ERC through grant SH-1852332. Author contact information: Claudia Noack, Department of Economics, University of Bonn0.40511
7Causal inference in network experiments: regression-based analysis and design-based properties0.40511
8AI-Assisted Variance Reduction in Randomized Experiments0.40511