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  • Search: subject:"Multiple Splitting,Assumption-Freeness"
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Year of publication
Subject
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Agnostic Inference 3 Causal Effects 3 Confidence Intervals 3 Machine Learning 3 Multiple Splitting,Assumption-Freeness 3 Quantification of Uncertainty 3 Sample Splitting 3 Uniformly Valid Inference 3 Variational P-values and Confidence Intervals 3 Artificial intelligence 2 Causality analysis 2 Financial market 2 Finanzmarkt 2 Interval estimation 2 Intervallschätzung 2 Kausalanalyse 2 Künstliche Intelligenz 2 Theorie 1 Theory 1
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Online availability
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Free 3
Type of publication
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Book / Working Paper 3
Type of publication (narrower categories)
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Working Paper 3 Arbeitspapier 2 Graue Literatur 2 Non-commercial literature 2
Language
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English 3
Author
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Chernozhukov, Victor 3 Demirer, Mert 3 Duflo, Esther 3 Fernández-Val, Iván 2 Fernandez-Val, Ivan 1
Published in...
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CEMMAP working papers / Centre for Microdata Methods and Practice 1 Working paper / National Bureau of Economic Research, Inc. 1 cemmap working paper 1
Source
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ECONIS (ZBW) 2 EconStor 1
Showing 1 - 3 of 3
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Generic machine learning inference on heterogenous treatment effects in randomized experiments
Chernozhukov, Victor; Demirer, Mert; Duflo, Esther; … - 2018
Persistent link: https://www.econbiz.de/10011882147
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Cover Image
Generic machine learning inference on heterogenous treatment effects in randomized experiments
Chernozhukov, Victor; Demirer, Mert; Duflo, Esther; … - 2017
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most...
Persistent link: https://www.econbiz.de/10011941541
Saved in:
Cover Image
Generic machine learning inference on heterogenous treatment effects in randomized experiments
Chernozhukov, Victor; Demirer, Mert; Duflo, Esther; … - 2017
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most...
Persistent link: https://www.econbiz.de/10011775335
Saved in:
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