Sample Selection Bias as a Specification Error¶
Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153-161.
Cited by¶
6 citations across 6 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Confounding
This sourceFrames selection bias as an omitted-variable specification error and introduces the two-step (Heckman) correction; relevant to the selection-vs-confounding distinction. (Bibliography only — link added.)
- Selection Bias
- The concept spans both experimental-design/statistics origins (Berkson's 1946 hospital-bias recognition, Neyman's sampling-selection discussion) and econometrics origins (Heckman's 1979 formal treatment and 2000 Nobel-winning sample-selection model), making it a dual-origin principle essential to causal inference, observational research, and meta-analysis
This sourceHeckman sample selection bias econometrics labor market wage estimation.
Supported in partVerified against the publisher's abstract
Heckman 1979's abstract supplies exactly one clause: the formal treatment of sample selection as a specification error; it does not mention Berkson, Neyman, a Nobel, or meta-analysis.
“This paper discusses the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or «omitted variables» bias. A simple consistent two stage estimator is considered”
- The concept spans both experimental-design/statistics origins (Berkson's 1946 hospital-bias recognition, Neyman's sampling-selection discussion) and econometrics origins (Heckman's 1979 formal treatment and 2000 Nobel-winning sample-selection model), making it a dual-origin principle essential to causal inference, observational research, and meta-analysis
- Vantage-Induced Omission
- Selection bias names the case where a samplable selection mechanism distorts data; vantage-induced omission generalises to institutional, instrumental, and positional selection, including cases where no samplable selection function exists at all — which stories a newsroom can reach is not a probabilistic sample.
This sourceSelection bias as a samplable selection mechanism distorting inference, the formal-statistical special case the prime generalizes beyond.
Supported in partVerified against the publisher's abstract
Heckman's abstract defines selection bias as bias from non-randomly selected samples in estimating behavioral relationships, backing the claim's first half but not its extension to institutional, instrumental, and positional selection.
“This paper discusses the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or «omitted variables» bias.”
- Selection bias names the case where a samplable selection mechanism distorts data; vantage-induced omission generalises to institutional, instrumental, and positional selection, including cases where no samplable selection function exists at all — which stories a newsroom can reach is not a probabilistic sample.
Mechanisms¶
- Holdout Ground-Truth Audit
- If the holdout is not truly shielded — if audited cases are, deliberately or not, easier ones, or ones the proxy already flagged — the audit inherits the proxy's blind spots and flatters it; this is selection bias, and it is the quiet killer of audit programs.
This sourceIdentifies nonrandom sample selection as a source of biased inference.
- If the holdout is not truly shielded — if audited cases are, deliberately or not, easier ones, or ones the proxy already flagged — the audit inherits the proxy's blind spots and flatters it; this is selection bias, and it is the quiet killer of audit programs.
- Revealed Preference Choice Log
- Its deepest failure mode is survivorship bias: the log only sees choices that were made
This sourceShows that behavioral outcomes can be observed only for units whose self-selection decision places them in the recorded sample.
- Its deepest failure mode is survivorship bias: the log only sees choices that were made
- Selection-Model Sensitivity Analysis
- Its strength is that it takes the mechanism seriously as an object of analysis: when the plausible story is "the outcome itself drove who we saw," a selection model states that story mathematically and quantifies its consequences, rather than assuming it away.
This sourceModels nonrandom observation through an explicit selection process and quantifies its consequences for estimating the outcome relationship.
- Its strength is that it takes the mechanism seriously as an object of analysis: when the plausible story is "the outcome itself drove who we saw," a selection model states that story mathematically and quantifies its consequences, rather than assuming it away.
Verification¶
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