Generic machine learning inference on heterogenous treatment effects in randomized experiments
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- Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments," Papers 1712.04802, arXiv.org, revised Nov 2022.
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More about this item
Keywords
Agnostic Inference; Machine Learning; Confidence Intervals; Causal Effects; Variational P-values and Confidence Intervals; Uniformly Valid Inference; Quantification of Uncertainty; Sample Splitting; Multiple Splitting; Assumption-Freeness;All these keywords.
JEL classification:
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2018-07-30 (Big Data)
- NEP-CMP-2018-07-30 (Computational Economics)
- NEP-EXP-2018-07-30 (Experimental Economics)
- NEP-MFD-2018-07-30 (Microfinance)
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