A Method for Measuring Treatment Effects on the Treated without Randomization
AbstractThis 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
Share & Cite This Article
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.
Swamy P, Hall SG, Tavlas GS, Chang I-L, Gibson HD, Greene WH, Mehta JS. A Method for Measuring Treatment Effects on the Treated without Randomization. Econometrics. 2016; 4(2):19.Chicago/Turabian Style
Swamy, P.A.V.B.; Hall, Stephen G.; Tavlas, George S.; Chang, I-Lok; Gibson, Heather D.; Greene, William H.; Mehta, Jatinder S. 2016. "A Method for Measuring Treatment Effects on the Treated without Randomization." Econometrics 4, no. 2: 19.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.