Tools for Intuition about Sample Selection Bias and Its Correction
Abstract
The authors provide mathematical tools to assist intuition about selection bias in concrete empirical analyses. These new tools do not offer a general solution to the selection bias problem; no method now does that. Rather, the techniques they present offer a new decomposition of selection bias. This decomposition permits an analyst to develop intuition and make reasoned judgments about the sources, severity, and direction of sample selection bias in a particular analysis. When combined with simulation results, also presented in this paper, their decomposition of bias also permits a reasoned, empirically-informed judgment of when the well-known two-step estimator of J. Heckman is likely to increase or decrease the accuracy of regression coefficient estimates. The authors also use simulations to confirm mathematical derivations
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Sociological surveys of sample selection bias tend to focus on cases where selection is affected by treatment or outcome, i.e., after treatment.
“Surveys of sample selection bias in sociology tend to emphasize examples where selection is affected by the treatment or the outcome, i.e., where sample selection occurs after treatment”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Stolzenberg & Relles 1997
There is currently no universal solution for estimating causal effects when endogenous selection is present.
“there is, at present, no universal solution to estimating causal effects in the presence of endogenous selection”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Stolzenberg & Relles 1997
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References 16 · Cited by 301
- Authors
- Stolzenberg, R. M., & Relles, D. A.
- ORCID
- Stolzenberg, Ross M.
- Venue
- American Sociological Review
- Publisher
- SAGE Publications
- Published
- 1997-06
- Volume
- 62
- Issue
- 3
- Pages
- 494
- ISSN
- 0003-1224 (ISSN-L)
- OpenAlex
- W2009120001
- Cited by
- 301
- References
- 16
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 8 · 2020: 9 · 2019: 6 · 2018: 5 · 2017: 9 · 2016: 5 · 2015: 26 · 2014: 13 · 2013: 17 · 2012: 14
- FWCI
- 10.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Sociological Review (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sample selection bias, regression coefficient bias, bias correction, simulation study
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