Targeted Undersmoothing.

By Christian Hansen, Damian Kozbur, Sanjog Misra. 2017

12 citations recorded by the source corpus.

This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for a broad class of functionals of sparse high-dimensional statistical models. These include dense functionals, which may potentially depend on all elements of an unknown high-dimensional parameter. The proposed confidence sets are based on an initially selected model and two additionally selected models, an upper model and a lower model, which enlarge the initially selected model. We illustrate application of the procedure in two empirical examples. The first example considers estimation of heterogeneous treatment effects using data from the Job Training Partnership Act of 1982, and the second example looks at estimating profitability from a mailing strategy based on estimated heterogeneous treatment effects in a direct mail marketing campaign. We also provide evidence on the finite sample performance of the proposed targeted undersmoothing procedure through a series of simulation experiments.

Subfields: Statistics and Probability

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Citations in the corpus

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, methodology): “We refer the reader to that paper for a more complete description of the data as well as results using other estimators (see also Hansen et al. (2017)).”
Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments (2017, unknown): “[31] proposed a sparsity based method called ”targeted undersmoothing” to perform inference on heterogeneous effects.”
High-dimensional econometrics and regularized GMM (2018, background): “o provide valid inference for many functional parameters, and [18] consider inference for many parameters in a high-dimensional linear model with errors in variables. Finally, [34], [127], [128], and [72] consider diﬀerent approaches which allow testing hypotheses about functionals that may involve ”