Nonparametric Bounds on Treatment Effects
By Charles F. Manski. 1989
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Guido W. Imbens
- Judea Pearl
- Susan Athey
- Victor Chernozhukov
- M. D. Cattaneo
- James J. Heckman
- Alberto Abadie
- C. Chaisemartin
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).”
- Causal inference in statistics: An overview (2009, background): “Looser bounds were derived earlier by Robins (1989) and Manski (1990). relative to the pair (X, Y ).”
- Fuzzy Differences-in-Differences (2015, methodology): “7 As emphasized in Manski (1997), it might be credible in some economic contexts, such as the analysis of the supply of a good which can be assumed to be an increasing function of its price.”
- More on monotone instrumental variables (2009, unknown): “To this end, Manski and Pepper (2000) introduced monotone instrumental variable (MIV) assumptions.”
- 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.”
- TREATMENT EFFECT HETEROGENEITY I N T HEORY AND PRACTICE (2004, unknown): “See Manski (1990) or Heckman and Vytlacil (2000).”
- What Would It Take to Change an Inference? Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences (2013, background): “Altonji, Elder, and Taber (2005) and Altonji, Conley, Elder, and Taber (2010) bounded estimated effects by drawing on information associated with observed confounds (cf. Manski, 1990, who bounds based on the maximum value of the outcome).”
- Overdispersion and Poisson Regression (2008, background): “In practice, it is likely that excess variation around fitted values results from specification errors in the systematic part of the Poisson regression model: omitted variables and incorrect functional forms (Box, 1976; Freedman, 1987; de Leeuw, 1994; Manski, 1990; Heckman, 1999; Berk, 2003).”