Statistics – Methodology
Scientific paper
2011-12-31
Statistics
Methodology
Scientific paper
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 $s < n$ unknown controls or series terms are needed to approximate the regression function accurately. The latter sparsity condition makes it possible to estimate the entire regression function as well as the average treatment effect by selecting an approximately the right set of controls using Lasso and related methods. We develop estimation and inference methods for the average treatment effect in this setting, proposing a novel "post double selection" method that provides attractive inferential and estimation properties. In our analysis, in order to cover realistic applications, we expressly allow for imperfect selection of the controls and account for the impact of selection errors on estimation and inference. In order to cover typical applications in economics, we employ the selection methods designed to deal with non-Gaussian and heteroscedastic disturbances. We illustrate the use of new methods with numerical simulations and an application to the effect of abortion on crime rates.
Belloni Alexandre
Chernozhukov Victor
Hansen Christian
No associations
LandOfFree
Estimation of Treatment Effects with High-Dimensional Controls does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Estimation of Treatment Effects with High-Dimensional Controls, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimation of Treatment Effects with High-Dimensional Controls will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-672704