multi-valued treatment effects
AbstractThe term multi-valued treatment effects refers to a collection of population parameters capturing the impact of a treatment variable on an outcome variable when the treatment takes multiple values. For example, in labour training programmes participants receive different hours of training or in anti-poverty programmes households receive different levels of transfers. Multi-valued treatments may be finite or infinite as well as ordinal or cardinal, and naturally extend the idea of binary treatment effects, leading to a large collection of treatment effects of interest in applications. The analysis of multi-valued treatment effects has several distinct features when compared to the analysis of binary treatment effects, including: (i) a comparison or control group is not always clearly defined, (ii) new parameters of interest arise that capture distinct phenomena such as nonlinearities or tipping points, (iii) correct statistical inference requires the joint estimation of all treatment effects (as opposed to the estimation of each treatment effect separately) in general, and (iv) efficiency gains in statistical inference may be obtained by exploiting known restrictions among the multi-valued treatment effects.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
This chapter was published in: Steven N. Durlauf & Lawrence E. Blume (ed.) , , pages , 2010, 2nd quarter update.
This item is provided by Palgrave Macmillan in its series The New Palgrave Dictionary of Economics with number v:4:year:2010:doi:3825.
Contact details of provider:
Web page: http://www.palgrave-journals.com/
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Carlos A. Flores & Oscar A. Mitnik, 2009.
"Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data,"
2010-10, University of Miami, Department of Economics.
- Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-9, University of Miami, Department of Economics.
- Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute for the Study of Labor (IZA).
- Carlos A. Flores & Alfonso Flores-Lagunes & Arturo Gonzalez & Todd C. Neumann, 2009. "Estimating the Effects of Lenght of Exposure to Traning Program: The Case of Job Corps," Working Papers 2010-3, University of Miami, Department of Economics.
- Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2012. "Estimating Conditional Average Treatment Effects," CEU Working Papers 2012_16, Department of Economics, Central European University, revised 20 Jul 2012.
- Carlos A. Flores & Oscar A. Mitnik, 2011. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," Working Papers 2011-10, University of Miami, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sheeja Sanoj).
If references are entirely missing, you can add them using this form.