Estimation of treatment effects with high-dimensional controls
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
|Date of creation:||31 Dec 2011|
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- 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.
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Econometric Society, vol. 80(6), pages 2369-2429, November.
- Alexandre Belloni & D. Chen & Victor Chernozhukov & Christian Hansen, 2010. "Sparse models and methods for optimal instruments with an application to eminent domain," CeMMAP working papers CWP31/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- 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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