Publication Type

Working Paper

Publication Date



One of the most well documented regularities in evaluation literature like returns to schooling(or funding for programs) is that several factors come together to confound the measurement of its effect. First, in observational studies the true return is often individual specific, and so it is almost impossible to use a traditional treatment effect models with randomly assigned treatment and control groups. This endogeneity in the model further exacerbates our inability to conduct such trials. Second, the problem is not a classical treatment effect measurement problem where we have discrete or more often binary treatments. Hence, techniques like measuring the Local Average Treatment Effect (LATE) cannot be implemented as it is not very well defined for the continuous treatment case. Third, a traditional 2SLS approach might be misleading because of the non-Gaussian nature of response distribution, in particular, if different quantiles of response have differential effects. However, their technique is also not defined for continuous treatments, and cannot measure if different distributions of the treatment might have different effects on the response variable. In this paper, we propose the effects of different multi-valued treatment variable after conditioning for other covariates.


Treatment Effect, Instrumental variable, Projection type estimator, Quantile Regression, Exogeneity, Monotonicity



Research Areas


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

Included in

Econometrics Commons