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the claim

Control function approaches and instrumental variables handle endogeneity differently under varying model assumptions

the verdict
SUPPORTED
the evidence backs this
Recorded sources
3 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that control function approaches and instrumental variables handle endogeneity differently under varying model assumptions?

Yes. Control function approaches and instrumental variables rely on distinct statistical restrictions (such as conditional mean independence versus conditional moment restrictions) and exhibit different performance characteristics, vulnerabilities, and consistency properties depending on model assumptions like linearity and heterogeneity.

Verdict: SUPPORTED · 3 sources for, 0 against · Last checked

The analysis

The retrieved papers examine both instrumental variable (IV) methods and control function (CF) approaches, directly addressing how these methods differ in assumptions, performance, and applicability under varying model conditions (such as nonlinearity, endogeneity types, and heterogeneity). Papers 3, 4, and 8 explicitly compare or differentiate how IV and CF techniques operate and perform under different assumptions. No papers refute the claim, leading to a verdict of SUPPORTED.

Evidence for · 3
A Generalized Non-Parametric Instrumental Variable-Control Function Approach to Estimation in Nonlinear Settings
2022
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Kim, K. & Petrin, A. (2022). A Generalized Non-Parametric Instrumental Variable-Control Function Approach to Estimation in Nonlinear Settings. doi.org. https://doi.org/10.1515/jem-2021-0038

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More for · 2
Weak instrumental variables due to ignored nonlinearities in panel data: A Super Learner Control Function estimator
2025
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Avila-Marquez, M. (2025). Weak instrumental variables due to ignored nonlinearities in panel data: A Super Learner Control Function estimator..

Widespread genetic effect heterogeneity impacts bias and power in nonlinear Mendelian randomization.
2026
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Wang, J. & Morrison, J. (2026). Widespread genetic effect heterogeneity impacts bias and power in nonlinear Mendelian randomization.. doi.org. https://doi.org/10.64898/2026.04.17.26351133

Receipt History
first checked05 Aug 2026
judged → SUPPORTED · 7905 Aug 2026
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