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Double/Debiased Machine Learning for Treatment and Structural Parameters
TLDR
This work revisits the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0 and proves that DML delivers point estimators that concentrate in a N^(-1/2)-neighborhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements.
Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
TLDR
A fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates is provided and its asymptotic validity under non-Gaussian, heteroscedastic disturbances is established.
An IV Model of Quantile Treatment Effects
The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications.
High-Dimensional Methods and Inference on Structural and Treatment Effects
TLDR
Using scanner datasets that record transaction-level data for households across a wide range of products, or text data where counts of words in documents may be wide range to text data, researchers are faced with a large set of potential variables formed by different ways of interacting and transforming the underlying variables.
Double/Debiased/Neyman Machine Learning of Treatment Effects
TLDR
The application of a generic double/de-biased machine learning approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods is illustrated.
SUPPLEMENTARY APPENDIX FOR \INFERENCE ON TREATMENT EFFECTS AFTER SELECTION AMONGST HIGH-DIMENSIONAL CONTROLS"
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text (arXiv:1201.0224).
Program evaluation and causal inference with high-dimensional data
TLDR
This paper shows that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters, and provides results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model.
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