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Displaying results 1 to 3 of 3.

  1. Light for visual artists
    understanding and using light in art & design
    Author: Yot, Richard
    Published: 2020
    Publisher:  Laurence King Publishing, London

    Location:
    Technische Universität München, Universitätsbibliothek
    Inter-library loan:
    Unlimited inter-library loan, copies and loan
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    Source: Union catalogues
    Language: English
    Media type: Book
    ISBN: 9781786274519; 1786274515
    Other identifier:
    9781786274519
    DDC Categories: 740
    Edition: Second edition
    Subjects: Licht <Motiv>; Kunst; Licht
    Other subjects: Lehrbuch; Lichtdesign; Lichtregie; Lichtsetzung; Tutorial
    Scope: 176 Seiten, Illustrationen, 28 cm x 21.6 cm
  2. Light for visual artists
    understanding and using light in art & design
    Author: Yot, Richard
    Published: 2020
    Publisher:  Laurence King Publishing, London

    Location:
    Universität der Künste Berlin, Universitätsbibliothek
    Inter-library loan:
    Unlimited inter-library loan, copies and loan
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781529432329
    DDC Categories: 740
    Edition: Second edition
    Subjects: Licht; Licht <Motiv>; Kunst
    Other subjects: Lehrbuch; Lichtdesign; Lichtregie; Lichtsetzung; Tutorial
    Scope: 1 Online-Ressource (139 Seiten), Illustrationen
  3. CATE meets ML
    conditional average treatment effect and machine learning
    Author: Jacob, Daniel
    Published: [2021]
    Publisher:  International Research Training Group 1792, Berlin

    For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with... more

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    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 744
    Inter-library loan:
    No inter-library loan

     

    For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory allows us to estimate not only the average but a personalized treatment effect - the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if the 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains metalearners, like the Doubly-Robust, the R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and the 401(k) example, we find a positive treatment effect for all observations but diverse evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/233509
    Series: IRTG 1792 discussion paper ; 2021, 005
    Subjects: Causal Inference; CATE; Machine Learning; Tutorial
    Scope: 1 Online-Ressource (circa 38 Seiten), Illustrationen