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A Method for Measuring Treatment Effects on the Treated without Randomization

P.A.V.B. Swamy (), Stephen G. Hall (), George Tavlas (), I-Lok Chang (), Heather Gibson (), William Greene () and Jatinder S. Mehta ()
Additional contact information
P.A.V.B. Swamy: Federal Reserve Board (Retired), 6333 Brocketts Crossing, Kingstowne, VA 22315, USA
Stephen G. Hall: Leicester University, Room Astley Clarke 116, University Road, Leicester LEI 7RH, UK
I-Lok Chang: Department of Mathematics and Statistics (Retired), The American University, Washington, DC 20016, USA
Jatinder S. Mehta: Department of Mathematics (Retired), Temple University, Philadelphia, PA 19122, USA

Econometrics, 2016, vol. 4, issue 2, 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.

Keywords: causality; real-world relationship; unique error term; treatment effect; non-experimental situation (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
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