Boosting algorithms are very popular in Machine Learning and have proven very useful for prediction and variable selection. Nevertheless in many applications the researcher is interested in inference on treatment effects or policy variables in a high-dimensional setting. Empirical researchers are more and more faced with rich datasets containing very many controls or instrumental variables, where variable selectionis challenging . In this paper we give results for the valid inference of a treatment effect after selecting from among very many control variables and the estimation of instrumental variables with potentially very many instrumentswhen post- or orthogonal L_2-Boosting is used for the variable selection. This setting allows for valid inference on low-dimensional components in a regression estimated with L_2-Boosting . We give  simulation results for the proposed methods and  an empirical application, in which we analyze the effectiveness of a pulmonary artery catheter .
 Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection . In this paper , we provide results for valid inference after post- or orthogonal L_2-Boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this, we establish new results for the rate of convergence of iterated post-L_2-Boosting and orthogonal L_2-Boosting in a high-dimensional setting similar to Lasso, i.e., under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis . We give extensive simulation results for the proposed methods and compare them with Lasso. In an empirical application, we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank .
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Boosting algorithms are very popular in Machine Learning and have proven very useful for prediction and variable selection. Nevertheless in many applications the researcher is interested in inference on treatment effects or policy variables in a high-dimensional setting.
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more and more
increasingly
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datasets containing very
data sets containing
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where variable selectionis challenging
making it essential to choose an appropriate approach to variable selection
R
we give results for the valid inference of a treatment effect after selecting from among very
, we provide results for valid inference after post- or orthogonal L_2-Boosting is used for variable selection. We consider treatment effects after selecting among
R
the estimation of instrumental variables with potentially very many instrumentswhen post- or
instrumental variable models with potentially many instruments. To achieve this, we establish new results for the rate of convergence of iterated post-L_2-Boosting and
R
is used for the variable selection. This setting allows for valid inference on low-dimensional components in a regression estimated with L_2-Boosting
in a high-dimensional setting similar to Lasso, i.e., under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis
A
extensive
A
compare them with Lasso. In
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in which we analyze the effectiveness of a pulmonary artery catheter
we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank