Structural Equations, Treatment Effects, and Econometric Policy Evaluation1
Abstract
This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametric functional form; to organize the literature on alternative estimators; and to explore what policy questions commonly used estimators in the treatment effect literature answer. A fundamental asymmetry intrinsic to the method of instrumental variables (IV) is noted. Recent advances in IV estimation allow for heterogeneity in responses but not in choices, and the method breaks down when both choice and response equations are heterogeneous in a general way.
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Angrist et al. (1996) and Heckman and Vytlacil (2005) refined the analysis by considering a variety of causal effect measures, each applicable to a special segment of the population.
“Angrist et al. (1996) and Heckman and Vytlacil (2005) further refined this analysis by considering a variety of causal effect measures, each applicable to a special (albeit non-identifiable and transient) segment of the population.”
From Causal inference in statistics: An overview (2009) · cites this paper as Heckman and Vytlacil (2005)
Cites
Identification and estimation of local average treatment effects
Imbens, G. W., & Angrist, J. D. (1994) · Econometrica · doi:10.2307/2951620 · Cited by 4,101 · see citing works
Characterizing Selection Bias Using Experimental Data
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998) · Econometrica · doi:10.2307/2999630 · Cited by 2,286 · see citing works
Self-citationGeneral Equilibrium Treatment Effects: A Study of Tuition Policy
Heckman, J., Lochner, L., & Taber, C. (1998) · doi:10.3386/w6426 · Cited by 289 · see citing works
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Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926 · see citing works
Regression discontinuity designs in economics
Lee, D. S., & Lemieux, T. (2010) · Journal of Economic Literature · doi:10.1257/jel.48.2.281 · Cited by 4,534 · see citing works
The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior
Heckman, J. J., Stixrud, J., & Urzua, S. (2006) · Journal of Labor Economics · doi:10.1086/504455 · Cited by 4,427 · see citing works
Self-citationCausal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336 · see citing works
Using matching, instrumental variables and control functions to estimate economic choice models
Heckman, J., & Navarro-Lozano, S. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023660 · Cited by 768 · see citing works
Self-citationAn Introduction to Causal Inference
Pearl, J. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1203 · Cited by 755 · see citing works
Estimating Marginal Returns to Education
Carneiro, P., Heckman, J. J., & Vytlacil, E. J. (2011) · American Economic Review · doi:10.1257/aer.101.6.2754 · Cited by 612 · see citing works
Self-citationInference on Counterfactual Distributions
(2013) · Econometrica · doi:10.3982/ecta10582 · Cited by 601 · see citing works
Dobbie, W., Goldin, J., & Yang, C. S. (2018) · American Economic Review · doi:10.1257/aer.20161503 · Cited by 564 · see citing works
Site Selection Bias in Program Evaluation *
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Bhuller, M., Dahl, G. B., Løken, K. V., & Mogstad, M. (2020) · Journal of Political Economy · doi:10.1086/705330 · Cited by 335 · see citing works
Who benefits from universal child care? Estimating marginal returns to early child care attendance
Cornelissen, T., Dustmann, C., Raute, A., & Schönberg, U. (2018) · Journal of Political Economy · doi:10.1086/699979 · Cited by 315 · see citing works
Program evaluation and causal inference with high-dimensional data
Belloni, A., Chernozhukov, V., Fern�ndez-Val, I., & Hansen, C. (2017) · Econometrica · doi:10.3982/ecta12723 · Cited by 317 · see citing works
Women's Empowerment in Action: Evidence from a Randomized Control Trial in Africa
Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., & Sulaiman, M. (2020) · American Economic Journal: Applied Economics · doi:10.1257/app.20170416 · Cited by 311 · see citing works
Bellon, M. R., Ntandou-Bouzitou, G. D., & Caracciolo, F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162535 · Cited by 146 · see citing works
Doubly robust uniform confidence band for the conditional average treatment effect function
Lee, S., Okui, R., & Whang, Y. (2017) · Journal of Applied Econometrics · doi:10.1002/jae.2574 · Cited by 67 · see citing works
Humlum, A., & Vestergaard, E. (2024) · doi:10.2139/ssrn.4827166 · Cited by 9 · see citing works
- Authors
- Heckman, J. J., & Vytlacil, E.
- Affiliations
- University of Chicago (US)
- Stanford University (US)
- ORCID
- Heckman, James J.
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2005-05
- Volume
- 73
- Issue
- 3
- Pages
- 669-738
- ISSN
- 0012-9682 (ISSN-L)
- 1468-0262
- OpenAlex
- W3121415235
- Cited by
- 1,220
- References
- 3
- Citations by year
- 2026: 43 · 2025: 54 · 2024: 59 · 2023: 91 · 2022: 70 · 2021: 87 · 2020: 80 · 2019: 63 · 2018: 73 · 2017: 82 · 2016: 66 · 2015: 47 · 2014: 56 · 2013: 50 · 2012: 44
- FWCI
- 39.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Econometrica (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Keywords
- marginal treatment effect, treatment effects, structural equations, policy evaluation, nonparametric estimation, semiparametric models, instrumental variables, heterogeneous reactions
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