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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Bibliography
Cattaneo, M.D. 2010. Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics 155: 138–154.
Florens, J.P., J.J. Heckman, C. Meghir, and E.J. Vytlacil. 2010. Identification of treatment effects using control functions in models with continuous, endogenous treatment and heterogeneous effects. Econometrica 76: 1191–1206.
Heckman, J.J., and E.J. Vytlacil. 2007. Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. In Handbook of econometrics, vol. 6B, ed. J.J. Heckman and E.E. Leamer, 4779–4874. Amsterdam: North-Holland.
Hirano, K., and G. Imbens. 2004. The propensity score with continuous treatments. In Applied bayesian modeling and causal inference from incomplete data perspectives, ed. A. Gelman and X.L. Meng. New York: Wiley.
Holland, P.W. 1986. Statistics and causal inference (with discussion). Journal of the American Statistical Association 81: 945–970.
Imai, K., and D.A. van Dyk. 2004. Causal inference with general treatment regimes: Generalizing the propensity score. Journal of the American Statistical Association 99: 854–866.
Imbens, G. 2000. The role of the propensity score in estimating dose–response functions. Biometrika 87: 706–710.
Imbens, G.W., and J.M. Wooldridge. 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47: 5–86.
Lechner, M. 2001. Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In Econometric evaluations of active labor market policies in Europe, ed. M. Lechner and F. Pfeiffer. Heidelberg: Physica.
Nekipelov, D. 2008. Endogenous multi-valued treatment effect model under monotonicity. Working paper, UC-Berkeley.
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Cattaneo, M.D. (2018). Multi-valued Treatment Effects. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2915
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2915
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