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Title: Estimation and inference of treatment effects with $L_2$-boosting in high-dimensional settings Authors:  Martin Spindler - University of Hamburg (Germany) [presenting]
Abstract: 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 data sets containing very many controls or instrumental variables where variable selection is challenging. We give results for valid inference of a treatment effect after selecting amongst very many control variables and instrumental variables estimation with potentially very many instruments when post- or orthogonal $L_2$-Boosting is used for variable selection. We give simulation results for the proposed methods and an empirical application.