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Machine learning estimation of heterogeneous causal effects : empirical Monte Carlo evidence

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Double machine learning for treatment and causal parameters

Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; … - 2016

random forests, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregates of these methods (e ….g. a hybrid of a random forest and lasso). We illustrate the application of the general theory through application to the …

Persistent link: https://www.econbiz.de/10011538313

Machine learning techniques applied to US army and navy data

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Artificial intelligence in asset management

Bartram, Söhnke M.; Branke, Jürgen; Motahari, Mehrshad - 2020 - This revision 01 April 2020

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A machine learning approach to improving occupational income scores

Saavedra, Martin; Twinam, Tate - In: Explorations in economic history : EEH 75 (2020)

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Predicting product return volume using machine learning methods

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BLP-2LASSO for aggregate discrete choice models with rich covariates : editor's choice

Gillen, Benjamin J.; Montero, Sergio; Moon, Hyungsik Roger - In: The econometrics journal 22 (2019) 3, pp. 262-281

Persistent link: https://www.econbiz.de/10012166760