Using matching, instrumental variables and control functions to estimate economic choice models
Abstract
This paper investigates four topics. (1) It examines the different roles played by the propensity score (the probability of selection into treatment) in matching, instrumental variable, and control function methods. (2) It contrasts the roles of exclusion restrictions in matching and selection models. (3) It characterizes the sensitivity of matching to the choice of conditioning variables and demonstrates the greater robustness of control function methods to misspecification of the conditioning variables. (4) It demonstrates the problem of choosing the conditioning variables in matching and the failure of conventional model selection criteria when candidate conditioning variables are not exogenous in a sense defined in this paper.
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Using simulation and parametric analysis, Heckman and Navarro-Lozano (2004) and Wooldridge (2009) confirmed the bias-raising potential of certain covariates in propensity-score methods.
“Using simulation and parametric analysis, Heckman and Navarro-Lozano (2004) and Wooldridge (2009) indeed confirmed the bias-raising potential of certain covariates in propensity-score methods.”
From Causal inference in statistics: An overview (2009) · cites this paper as Heckman and Navarro-Lozano, 2004
Heckman and Navarro-Lozano (2004) and Wooldridge (2009) confirmed the bias-raising potential of certain covariates in propensity-score methods.
“Using simulation and parametric analysis, Heckman and Navarro-Lozano (2004) and Wooldridge (2009) indeed confirmed the bias-raising potential of certain covariates in propensity-score methods.”
From An Introduction to Causal Inference (2010) · cites this paper as Heckman and Navarro-Lozano (2004)
The analysis modeled the hazard of having high sugar availability rates in each country and used this constructed hazard variable in a Heckman selection model to explicitly control for potential unobserved selection bias.
“We also modeled the hazard of having high sugar availability rates in each country, and used this constructed hazard variable to explicitly control for potential unobserved selection bias (a “Heckman selection model”, see Text S1) [29].”
From The relationship of sugar to population-level diabetes prevalence: an econometric analysis of repeated cross-sectional data (2013) · cites this paper as [29]
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- Authors
- Heckman, J., & Navarro-Lozano, S.
- Affiliations
- American Bar Foundation (US)
- ORCID
- Heckman, James
- Venue
- Review of Economics and Statistics
- Publisher
- MIT Press
- Published
- 2004-02
- Volume
- 86
- Issue
- 1
- Pages
- 30-57
- ISSN
- 0034-6535 (ISSN-L)
- 1530-9142
- OpenAlex
- W3122304594
- Cited by
- 768
- References
- 62
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 32 · 2023: 36 · 2022: 40 · 2021: 31 · 2020: 29 · 2019: 29 · 2018: 38 · 2017: 33 · 2016: 40 · 2015: 39 · 2014: 43 · 2013: 43 · 2012: 43
- FWCI
- 32.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Review of Economics and Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Social Sciences › Education › School Choice and Performance
- Keywords
- propensity score, matching, instrumental variables, control functions, exclusion restriction, conditioning variables, model selection, selection model
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