Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171881
Authors: 
Swamy, Paravastu A. V. B.
Hall, Stephen G.
Tavlas, George S.
Chang, I-Lok
Gibson, Heather D.
Greene, William H.
Mehta, Jatinder S.
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 2 [Pages:] 1-23
Abstract: 
This paper contributes to the literature on the estimation of causal effects by providing an analytical formula for individual specific treatment effects and an empirical methodology that allows us to estimate these effects. We derive the formula from a general model with minimal restrictions, unknown functional form and true unobserved variables such that it is a credible model of the underlying real world relationship. Subsequently, we manipulate the model in order to put it in an estimable form. In contrast to other empirical methodologies, which derive average treatment effects, we derive an analytical formula that provides estimates of the treatment effects on each treated individual. We also provide an empirical example that illustrates our methodology.
Subjects: 
causality
real-world relationship
unique error term
treatment effect
non-experimental situation
JEL: 
C13
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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