Learning about Treatment Effects from Experiments with Random Assignment of Treatments

By Charles F. Manski. 1996

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “…treatment in settings with non-compliance an alternative to making additional assumptions, is to focus on getting ranges of values for the estimand that are consistent with the data in a bounds or partial identification approach in a line of research associated with Manski (1990, 1996, 2003, 2013).”
Statistical treatment rules for heterogeneous populations (2003, background): “Maintaining the utilitarian paradigm, Manski (1996, 2000) showed how identification problems generate ambiguity about the identity of optimal treatment choices.”
WORKING PAPER SERIES BETTER LATE THAN NOTHING : SOME COMMENTS ON DEATON ( 2009 ) AND HECKMAN AND URZUA (background): “(Manski, 1996, page 714).”
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, methodology): “Following Balke and Pearl (1997), Manski (1989, 1990, 1996, 2003), and Robins (1989), we can use the identified combinations 175 This definition of ξ assumes that ξ0 = · · · = ξ −1 = 1.”
The dynamics of behavior change: Evidence from energy conservation (2016, background): “In many empirical settings, scholars often assume that treatment assigned equals treatment received (Manski 1996).”
BAYESIAN ASPECTS OF TREATMENT CHOICE (2011, background): “Manski (1990, 1996) discusses the lack of point identification for average treatment effects, and Manski (2000) discusses implications for decision making.”
Better Late than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) (2010, background): “(Manski, 1996, page 714).”
Immigrating to Opportunity: Estimating the Effect of School Quality Using a Natural Experiment on Ethiopians in Israel (2004, unknown): “Angrist, Joshua D.; Imbens, Guido W. and Rubin, Donald B. Identification of Causal Effects Using Instrumental Variables, Journal of the American Statistical Association, 91(434), June 1996, pages 444- 455.”
Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture (2001, unknown): “For problems with social experiments, see Heckman (1992), Heckman and Smith (1993, 1995), Manski (1996), Heckman, LaLonde, and Smith (1999), and Heckman and Vytlacil (2001a). nobel lecture 733 of social programs put in place by the modern welfare state and for interpreting empirical economic…”