Inference on Treatment Effects after Selection amongst High-Dimensional Controls

Inference on Treatment Effects after Selection amongst High-Dimensional Controls

We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances where the number of controls may be much larger than the sample...

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Bibliographic Details
Main Authors: Belloni, Alexandre, Chernozhukov, Victor, Hansen, Christian
Format: Working Paper
Published: Cambridge, MA: Department of Economics, Massachusetts Institute of Technology 2012
Subjects:
treatment effects
partially linear model
high-dimensional-sparse regression
inference under imperfect model selection
uniformly valid inference after model selection
Online Access:http://hdl.handle.net/1721.1/71531

Internet

http://hdl.handle.net/1721.1/71531

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