A Method for Measuring Treatment Effects on the Treated without Randomization
P. A. V. B. Swamya (),
Stephen Hall (),
George Tavlas (),
I. Chang (),
Heather Gibson (),
William Greene () and
J. S. Mehta ()
No 16/02, Discussion Papers in Economics from Department of Economics, University of Leicester
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 treatment effects on each treated individual. We also provide an empirical example that illustrates our methodology.
Keywords: Causality; Real-world relationship; Unique error term; Treatment effect; Non-experimental situation (search for similar items in EconPapers)
JEL-codes: C13 C51 (search for similar items in EconPapers)
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Journal Article: A Method for Measuring Treatment Effects on the Treated without Randomization (2016)
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