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Sökning: WFRF:(de Luna Xavier)

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  • de Luna, Xavier, et al. (författare)
  • Covariate selection for the non-parametric estimation of an average treatment effect
  • 2011
  • Ingår i: Biometrika. - : Oxford University Press. - 0006-3444 .- 1464-3510. ; 98:4, s. 861-875
  • Tidskriftsartikel (refereegranskat)abstract
    • Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance. It is tempting to include many covariates in the controlling set to try to make the assumption of an unconfounded treatment realistic. Including unnecessary covariates is suboptimal when the effect of a binary treatment is estimated nonparametrically. For instance, when using a n1/2-consistent estimator, a loss of efficiency may result from using covariates that are irrelevant for the unconfoundedness assumption. Moreover, bias may dominate the variance when many covariates are used. Embracing the Neyman–Rubin model typically used in conjunction with nonparametric estimators of treatment effects, we characterize subsets from the original reservoir of covariates that are minimal in the sense that the treatment ceases to be unconfounded given any proper subset of these minimal sets. These subsets of covariates are shown to be identified under mild assumptions. These results lead us to propose data-driven algorithms for the selection of minimal sets of covariates.
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  • de Luna, Xavier, et al. (författare)
  • Exogeneity in structural equation models
  • 2006
  • Ingår i: Journal of Econometrics. - : Elsevier BV. - 0304-4076. ; 132:2, s. 527-543
  • Tidskriftsartikel (refereegranskat)abstract
    • The practical relevance of several concepts of exogeneity of treatments for the estimation of causal parameters based on observational data are discussed. We show that the traditional concepts, such as strong ignorability and weak and super-exogeneity, are too restrictive if interest lies in average effects (i.e. not on distributional effects of the treatment). We suggest a new definition of exogeneity, KL-exogeneity. It does not rely on distributional assumptions and is not based on counterfactual random variables. As a consequence it can be empirically tested using a proposed test that is simple to implement and is distribution-free.
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37.
  • de Luna, Xavier, et al. (författare)
  • Graphical diagnostics of endogeneity
  • 2008
  • Ingår i: Advances in Econometrics. - 0731-9053. ; 21, s. 149-168
  • Tidskriftsartikel (refereegranskat)
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38.
  • de Luna, Xavier, et al. (författare)
  • Graphical diagnostics of endogeneity
  • 2008
  • Ingår i: Modelling and Evaluating Treatment Effects in Econometrics. - : Emerald Group Publishing Limited. - 9780762313808 - 9781849505239 ; , s. 147-166
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. Moreover, the sign of the bias implied by the endogeneity may be deducible through such graphs. In general, instrumental variables are needed to implement the graphical test. However, when a continuous or ordered (e.g. years of schooling) variable is suspected to be endogenous, a graphical test for misspecification due to endogeneity (e.g. self-selection) can be obtained without instrumental variables.
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