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Causal inference for binary regression with observational data

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  • Austin Nichols

    ()
    (Urban Institute)

Abstract

Special problems arise when trying to do causal inference for binary regression with observational data; we will examine some of these problems and critically examine several common and not-so-common solutions.

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File URL: http://fmwww.bc.edu/repec/chic2011/chi11_nichols.pdf
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Bibliographic Info

Paper provided by Stata Users Group in its series CHI11 Stata Conference with number 6.

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Date of creation: 20 Jul 2011
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Handle: RePEc:boc:chic11:6

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Web page: http://stata.com/meeting/chicago11/
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  1. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc.
  2. Azeem M. Shaikh & Edward J. Vytlacil, 2011. "Partial Identification in Triangular Systems of Equations With Binary Dependent Variables," Econometrica, Econometric Society, vol. 79(3), pages 949-955, 05.
  3. Parke Wilde & Mark Nord, 2005. "The Effect of Food Stamps on Food Security: A Panel Data Approach ," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 27(3), pages 425-432.
  4. Joachim Wilde, 2005. "A note on GMM-estimation of probit models with endogenous regressors," IWH Discussion Papers 4, Halle Institute for Economic Research.
  5. Austin Nichols, 2009. "Causal inference," DC09 Stata Conference 8, Stata Users Group.
  6. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
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