Information-Theoretic Deconvolution Approximation of Treatment Effect Distribution
AbstractThis study proposes an information-theoretic deconvolution method to approximate the entire distribution of individual treatment effect. This method uses higher-order information implied by the standard average treatment effect estimator to construct a maximum entropy approximation to the treatment effect distribution. This method is able to approximate the underlying distribution even if it is entirely random or dependent on unobservable covariates. The asymptotic properties of the proposed estimator are discussed. This estimator is shown to minimize the Kullback-Leibler distance between the underlying distribution and the approximations. Monte Carlo simulations and experiments with real data demonstrate the efficacy and flexibility of the proposed deconvolution estimator. This method is applied to data from the U.S. Job Training Partnership Act (JTPA) program to estimate the distribution of its impact on individual earnings.
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Bibliographic InfoPaper provided by Institute of Industrial Relations, UC Berkeley in its series Institute for Research on Labor and Employment, Working Paper Series with number qt6bm6n30x.
Date of creation: 09 May 2007
Date of revision:
Other versions of this item:
- Wu, Ximing & Perloff, Jeffrey M., 2007. "Information-Theoretic Deconvolution Approximation of Treatment Effect Distribution," Institute for Research on Labor and Employment, Working Paper Series qt9vd036zx, Institute of Industrial Relations, UC Berkeley.
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