Simple Estimators for Treatment Parameters in a Latent-Variable Framework

By James Heckman, J. Tobias, E. Vytlacil. Review of Economics and Statistics, 2003

146 citations recorded by the source corpus.

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Citations in the corpus

Ability Sorting and the Returns to College Major (2004, methodology): “27 Keane and Wolpin (1994) present an alternative method which does not involve discretizing the error term. Their method involves approximating the integrals with di erent functions. Keane and Wolpin (1997) use this method in their model of career decisions.”
Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs (2005, background): “Heckman et al. (2003) follow this strategy in a related, linear equation context.”
Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation (2007, background): “Useful surveys of the econometrics of these models include Maddala (1983), Amemiya (1985), Ruud (2000) and Wooldridge (2002).”
Copula-Based Maximum-Likelihood Estimation of Sample-Selection Models (2013, background): “Interested readers are referred to the study by Heckman, Tobias, and Vytlacil (2003), on which the discussion of this subsection is based.”
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments (2007, background): “and thus the Manski (1990) upper bound collapses to the Heckman and Vytlacil (1999) upper bound under assumption S.”
Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond (2005, unknown): “Aakvik, Heckman, and Vytlacil (2005) and Heckman, Tobias, and Vytlacil (2001, 2003) derive all of the treatment parameters and distributions of treatment parameters for several parametric models including the normal.”
1. The Scientific Model of Causality (2005, background): “…of K0(P) and K1(P) by making distributional assumptions (e.g., normality or other conventional distributional assumptions about (U0, U1, UV); see Heckman, Tobias, and Vytlacil 2001, 2003), this is not an intrinsic feature of the method and there are many non-normal and semiparametric versions…”
Instrumental variables estimation of the average treatment effect in the correlated random coefficient model (2008, background): “(1999), and Heckman, Tobias, and Vytlacil (2003) .”