Local instrumental variables and latent variable models for identifying and bounding treatment effects

Heckman, James J.; Vytlacil, Edward

doi:10.1073/pnas.96.8.4730

Cited by 521 publications

(671 citation statements)

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“…The concept of MTE was first introduced by Bjorklund and Moffitt (1987). Carneiro et al (2001), Heckman et al (2006), and Heckman and Vytlacil (2007) develop the theoretical framework of MTE estimation for the returns to schooling; derive the identification of the average treatment effect (ATE), treatment on the treated (TT), and treatment on the untreated (TUT); and also provide the empirical applications of these methods for U.S. data. Heckman and Li (2003) and Wang et al (2007) apply these methods to the estimation of the returns to education in China.…”

Section: Heterogeneous Returns To Higher Educationmentioning

confidence: 99%

“…Correspondingly, MTE measured for large values of u s (close to 1) shows the returns to education for people less likely to obtain higher education, based on their unobservable characteristics. Heckman and Vytlacil (1999), Heckman et al (2006), and Heckman and Vytlacil (2007) show that ATE, TT and TUT could be determined as the weighted averages of MTE, according to the formulas (25), described in section 3 of the Technical Appendix. The weights 16 Note that in considering the population effects, the effect of the sorting on gains can be based on the observable characteristics if E(X|S = 1) = E(X|S = 0) = E(X), in other words, if people sort themselves based on the observable characteristics.…”

Section: Returns To Education: Model With Essential Heterogeneitymentioning

confidence: 99%

“…Second, the paper uses the recently developed non-parametric model with essential heterogeneity to identify marginal returns to education -the heterogeneous returns that vary with the level of unobserved individual characteristics. This estimation is based on the research of Carneiro et al (2001), Heckman et al (2006), and Heckman and Vytlacil (2007), who develop the theoretical and empirical framework for the marginal treatment effects (MTE) estimations. The main advantage of using this model is that both the heterogeneity of the returns to education and the selection of individuals based on these heterogeneous returns can be accounted for.…”

Section: Introductionmentioning

confidence: 99%

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Expansion of higher education, employment and wages: Evidence from the Russian Transition

Kyui

2016

Labour Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. iii Terms of use: Documents in AbstractThis paper analyzes the effects of an educational system expansion on labour market outcomes, drawing upon a 15-year natural experiment in the Russian Federation. Regional increases in student intake capacities in Russian universities, a result of educational reforms, provide a plausibly exogenous variation in access to higher education. Additionally, the gradual nature of this expansion allows for estimation of heterogeneous returns to education for individuals who successfully took advantage of increasing educational opportunities. Using simultaneous equations models and a nonparametric model with essential heterogeneity, the paper identifies strong positive returns to education in terms of employment and wages. Marginal returns to higher education are estimated to decline for lower levels of individual unobserved characteristics that positively influence higher education attainment. Finally, the returns to higher education are found to decrease for those who, as a result of the reforms, increasingly pursued higher education.

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Section: Heterogeneous Returns To Higher Educationmentioning

confidence: 99%

Section: Returns To Education: Model With Essential Heterogeneitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Expansion of higher education, employment and wages: Evidence from the Russian Transition

Kyui

2016

Labour Economics

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“…3 Some recent surveys provide insightful discussions about MTE, see for example Blundell and Costa Dias (2009) who discuss MTE among a range of alternative policy evaluation approaches, French and Taber (2011) who discuss treatment effects and MTE and its relation to the Roy model, and the excellent, comprehensive, but technical treatments of MTE in Heckman and Vytlacil (2007) and Heckman, Urzúa and Vytlacil (2006), based on the earlier work by Heckman and Vytlacil (1999, 2001a, 2001b.…”

Section: Introductionmentioning

confidence: 99%

“…The concept of the marginal treatment effect MTE was first introduced by Björklund and Moffitt (1987) in the context of a multivariate-normal switching regression model, in which they defined the "marginal gain" as the gain from treatment for individuals who are shifted into (or out of) treatment by a marginal change in the cost of treatment (i.e., the instrument). It was extended in a series of papers by Heckman and Vytlacil (1999, 2001b, 2007 who define the MTE as the gain from treatment for individuals shifted into (or out of) treatment by a marginal change in the propensity score (i.e., the predicted probability of treatment, which is a function of the instrument), develop non-parametric estimation methods, and clarify the connection of the switching regime self-selection model and of MTE with IV and LATE.…”

Section: Introductionmentioning

confidence: 99%

From LATE to MTE: Alternative methods for the evaluation of policy interventions

et al. 2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Compared to the existing surveys on the subject, our paper is less technical and speaks to the applied economist with a solid basic understanding of econometric techniques who would like to use MTE estimation. Our framework of analysis is a generalized Roy model based on the potential outcomes framework, within which we define different treatment effects of interest, and review the well-known case of IV estimation with a discrete instrument resulting in a local average treatment effect (LATE). Turning to IV estimation with a continuous instrument we demonstrate that the 2SLS estimator may be viewed as a weighted average of LATEs, and discuss MTE estimation as an alternative and more informative way of exploiting a continuous instrument. We clarify the assumptions underlying the MTE framework and illustrate how the MTE estimation is implemented in practice. Terms of use: Documents inJEL-Codes: C260, I260.

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Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals

Dahabreh,

Bibbins-Domingo

2024

JAMA

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ImportanceMany medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals.ObservationsA structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable?Conclusions and RelevanceAdoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

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Local instrumental variables and latent variable models for identifying and bounding treatment effects

Cited by 521 publications

References 13 publications

Expansion of higher education, employment and wages: Evidence from the Russian Transition

Expansion of higher education, employment and wages: Evidence from the Russian Transition

From LATE to MTE: Alternative methods for the evaluation of policy interventions

Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals

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