Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations
This paper reviews strategies that allow one to identify the effects of policy interventions on the unconditional or conditional distribution of the outcome of interest. This distiction is irrelevant when one focuses on average treatment effects since identifying assumptions typically do not affect the parameter's interpretation. Conversely, finding the appropriate answer to a research question on the effects over the distribution requires particular attention in the choice of the identification strategy. Indeed, quantiles of the conditional and unconditional distribution of a random variable carry a different meaning even if identification of both these set of parameters may require conditioning on observed covariates.
|Date of creation:||Dec 2012|
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- David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers 816, RAND Corporation.
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CeMMAP working papers
CWP32/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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- Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
- repec:ran:wpaper:824 is not listed on IDEAS
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