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Econometrics 2016, 4(2), 19; doi:10.3390/econometrics4020019

A Method for Measuring Treatment Effects on the Treated without Randomization

1
Federal Reserve Board (Retired), 6333 Brocketts Crossing, Kingstowne, VA 22315, USA
2
Leicester University, Room Astley Clarke 116, University Road, Leicester LEI 7RH, UK
3
Bank of Greece, 21 El. Venizelos Ave., 10250 Athens, Greece
4
Monetary Policy Council, Bank of Greece, 21 El. Venizelos Ave., 10250 Athens, Greece
5
Department of Mathematics and Statistics (Retired), The American University, Washington, DC 20016, USA
6
Economic Research Department, Bank of Greece, 21 El. Venizelos Ave., 10250 Athens, Greece
7
Department of Economics, New York University, 44 West Fourth Street, 7–90 New York, NY 10012, USA
8
Department of Mathematics (Retired), Temple University, Philadelphia, PA 19122, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Fredj Jawadi, Tony S. Wirjanto, Marc S. Paolella and Nuttanan Wichitaksorn
Received: 13 July 2015 / Revised: 22 February 2016 / Accepted: 9 March 2016 / Published: 25 March 2016
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
View Full-Text   |   Download PDF [633 KB, uploaded 25 March 2016]   |  

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. View Full-Text
Keywords: causality; real-world relationship; unique error term; treatment effect; non-experimental situation causality; real-world relationship; unique error term; treatment effect; non-experimental situation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Swamy, P.; Hall, S.G.; Tavlas, G.S.; Chang, I.-L.; Gibson, H.D.; Greene, W.H.; Mehta, J.S. A Method for Measuring Treatment Effects on the Treated without Randomization. Econometrics 2016, 4, 19.

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