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Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis
Open accessAbstract
Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors? In practice, researchers typically assume that the sample is randomly drawn from a large population of interest and report standard errors that are designed to capture sampling variation. This is common even in applications where it is difficult to articulate what that population of interest is, and how it differs from the sample. In this article, we explore an alternative approach to inference, which is partly design‐based. In a design‐based setting, the values of some of the regressors can be manipulated, perhaps through a policy intervention. Design‐based uncertainty emanates from lack of knowledge about the values that the regression outcome would have taken under alternative interventions. We derive standard errors that account for design‐based uncertainty instead of, or in addition to, sampling‐based uncertainty. We show that our standard errors in general are smaller than the usual infinite‐population sampling‐based standard errors and provide conditions under which they coincide.
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Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
White, H. (1980) · Econometrica · doi:10.2307/1912934 · Cited by 25,861
(2013) · American Economic Review · doi:10.1257/aer.103.5.i · Cited by 15,100
(2002) · Cited by 13,211
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2008) · doi:10.2307/j.ctvcm4j72 · Cited by 5,051
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Self-citationMaximum Likelihood Estimation of Misspecified Models
White, H. (1982) · Econometrica · doi:10.2307/1912526 · Cited by 4,098
Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Kojima, K.-I. (1969) · Europe PMC (PubMed Central) · Cited by 3,648
Rosenbaum, P. R. (2002) · Springer Series in Statistics · doi:10.1007/978-1-4757-3692-2 · Cited by 2,394
MacKinnon, J. G., & White, H. (1985) · Journal of Econometrics · doi:10.1016/0304-4076(85)90158-7 · Cited by 1,879
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Splawa-Neyman, J., Dabrowska, D. M., & Speed, T. P. (1990) · Statistical Science · doi:10.1214/ss/1177012031 · Cited by 1,627
When Should You Adjust Standard Errors for Clustering?
Abadie, A., Athey, S., Imbens, G., & Wooldridge, J. (2017) · National Bureau of Economic Research · doi:10.3386/w24003 · Cited by 1,360
Self-citationInstruments, Randomization, and Learning about Development
Deaton, A. (2010) · Journal of Economic Literature · doi:10.1257/jel.48.2.424 · Cited by 1,286
Davidson, J. E. H. (1994) · Oxford University Press eBooks · doi:10.1093/0198774036.001.0001 · Cited by 1,157
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
Lin, W. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas583 · Cited by 641
Using Least Squares to Approximate Unknown Regression Functions
White, H. (1980) · International Economic Review · doi:10.2307/2526245 · Cited by 571
Angrist, J. D. (1998) · Econometrica · doi:10.2307/2998558 · Cited by 490
On regression adjustments to experimental data
Freedman, D. A. (2008) · Advances in Applied Mathematics · doi:10.1016/j.aam.2006.12.003 · Cited by 417
Learning Through Noticing: Theory and Evidence from a Field Experiment *
Hanna, R., Mullainathan, S., & Schwartzstein, J. (2014) · The Quarterly Journal of Economics · doi:10.1093/qje/qju015 · Cited by 325
Public Policy in an Uncertain World: Analysis and Decisions
Manski, C. F. (2013) · Harvard University Press eBooks · doi:10.4159/harvard.9780674067547 · Cited by 240
Why you Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers*
Farber, H. S. (2015) · The Quarterly Journal of Economics · doi:10.1093/qje/qjv026 · Cited by 239
Reference-Dependent Job Search: Evidence from Hungary*
DellaVigna, S., Lindner, A., Reizer, B., & Schmieder, J. F. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx015 · Cited by 205
Muralidharan, K., & Niehaus, P. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.4.103 · Cited by 194
Does Regression Produce Representative Estimates of Causal Effects?
Aronow, P. M., & Samii, C. (2016) · American Journal of Political Science · doi:10.1111/ajps.12185 · Cited by 192
The Response of Drug Expenditure to Nonlinear Contract Design: Evidence from Medicare Part D *
Einav, L., Finkelstein, A., & Schrimpf, P. (2015) · The Quarterly Journal of Economics · doi:10.1093/qje/qjv005 · Cited by 182
Keels, M., Duncan, G. J., Deluca, S., Mendenhall, R., & Rosenbaum, J. (2005) · Demography · doi:10.1353/dem.2005.0005 · Cited by 181
Donohue, J. J., Aneja, A., & Weber, K. D. (2019) · Journal of Empirical Legal Studies · doi:10.1111/jels.12219 · Cited by 170
Manski, C. F., & Pepper, J. V. (2018) · Review of Economics and Statistics · doi:10.1162/rest_a_00689 · Cited by 158
ASYMPTOTIC PROPERTIES OF WEIGHTED M-ESTIMATORS FOR STANDARD STRATIFIED SAMPLES
Wooldridge, J. M. (2001) · Econometric Theory · doi:10.1017/s0266466601172075 · Cited by 125
Self-citationSamii, C., & Aronow, P. M. (2011) · Statistics & Probability Letters · doi:10.1016/j.spl.2011.10.024 · Cited by 61
SHARP BOUNDS ON THE VARIANCE IN RANDOMIZED EXPERIMENTSPreprint
Aronow, P. M., GREEN, D. P., & Lee, D. (2016) · Cited by 60
Finite Population Causal Standard Errors
Abadie, A., Athey, S. C., Imbens, G. W., & Wooldridge, J. M. (2014) · National Bureau of Economic Research · doi:10.3386/w20325 · Cited by 58
Self-citationSharp bounds on the variance in randomized experiments
Aronow, P. M., Green, D. P., & Lee, D. K. K. (2014) · The Annals of Statistics · doi:10.1214/13-aos1200 · Cited by 49
Regression-assisted inference for the average treatment effect in paired experiments
Fogarty, C. B. (2018) · Biometrika · doi:10.1093/biomet/asy034 · Cited by 49
Inference for Misspecified Models With Fixed Regressors
Abadie, A., Imbens, G. W., & Zheng, F. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.928218 · Cited by 46
Self-citationEstimation of the Conditional Variance in Paired Experiments
ABADIE, & IMBENS (2008) · Annales d Économie et de Statistique · doi:10.2307/27917244 · Cited by 38
On randomization-based and regression-based inferences for 2K factorial designs
Lu, J. (2016) · Statistics & Probability Letters · doi:10.1016/j.spl.2016.01.010 · Cited by 34
A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares EstimandsPreprint
Słoczyński, T. (2018) · RePEc: Research Papers in Economics · Cited by 20
Research works printed but not linked: 2
- Goldberger, 1991, A Course in Econometrics
- Huber, 1967, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 221
Cited by
What's trending in difference-in-differences? A synthesis of the recent econometrics literature
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2023.03.008 · Cited by 2,027
Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
Abadie, A. (2021) · Journal of Economic Literature · doi:10.1257/jel.20191450 · Cited by 1,486
Self-citationWhen Should You Adjust Standard Errors for Clustering?
Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2022) · The Quarterly Journal of Economics · doi:10.1093/qje/qjac038 · Cited by 1,460
Self-citationDesign-based analysis in Difference-In-Differences settings with staggered adoption
Athey, S., & Imbens, G. W. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.012 · Cited by 917
Self-citationCluster-robust inference: A guide to empirical practice
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.001 · Cited by 407
Metadata
- Authors
- Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M.
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2020
- Volume
- 88
- Issue
- 1
- Pages
- 265-296
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2963737518
- Cited by
- 285
- References
- 40
- FWCI
- 27.98
- Type
- journal article
- Language
- en
- Open access
- green
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