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Estimation of treatment effects with high-dimensional controls

  • Alexandre Belloni

    (Institute for Fiscal Studies)

  • Victor Chernozhukov

    ()

    (Institute for Fiscal Studies and MIT)

  • Christian Hansen

    (Institute for Fiscal Studies and Chicago GSB)

We propose methods for inference on the average effect of a treatment on a scalar outcome in the presence of very many controls. Our setting is a partially linear regression model containing the treatment/policy variable and a large number p of controls or series terms, with p that is possibly much larger than the sample size n, but where only s

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File URL: http://cemmap.ifs.org.uk/wps/cwp4211.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP42/11.

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Date of creation: Dec 2011
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Handle: RePEc:ifs:cemmap:42/11
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  1. A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.
  2. Newey, Whitney K., 1997. "Convergence rates and asymptotic normality for series estimators," Journal of Econometrics, Elsevier, vol. 79(1), pages 147-168, July.
  3. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
  4. Kane, John, 1997. "Myth and measurement: The new economics of the minimum wage : David Card and Alan B. Krueger, New Jersey: Princeton University Press, 1995, x + 422," International Review of Economics & Finance, Elsevier, vol. 6(2), pages 219-222.
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