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multi-valued treatment effects

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  • Matias D. Cattaneo

Abstract

The 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.

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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.

Handle: RePEc:pal:dofeco:v:4:year:2010:doi:3825

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Related research

Keywords: causal inference; generalised propensity score; identification; matching estimators; program evaluation; semiparametric estimation; semiparametric efficiency; treatment effects; unconfoundedness;

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Cited by:
  1. Carlos A. Flores & Oscar A. Mitnik, 2013. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1691-1707, December.
  2. 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.
  3. 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.
  4. 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.

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