Dandl, S. und Bischl, B. (2023) Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations [cd]. Universitätsbibliothek der Ludwig-Maximilians-Universität. doi:10.5282/edoc.32947.
Chicago Manual of Style 17th edition (full note)Dandl, Susanne, und Bernd Bischl. „Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations“. Cd. Universitätsbibliothek der Ludwig-Maximilians-Universität, [2023?], Universitätsbibliothek der Ludwig-Maximilians-Universität, [2023?]. https://doi.org/10.5282/edoc.32947.
American Psychological Association 7th editionDandl, S., & Bischl, B. (ca. 2023). Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations [Universitätsbibliothek der Ludwig-Maximilians-Universität; Cd]. https://doi.org/10.5282/edoc.32947
Modern Language Association 9th editionDandl, S., und B. Bischl. Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations. cd, Universitätsbibliothek der Ludwig-Maximilians-Universität, 2023, https://doi.org/10.5282/edoc.32947.
ISO-690 (author-date, Deutsch)DANDL, Susanne und Bernd BISCHL, 2023. Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations. München: Universitätsbibliothek der Ludwig-Maximilians-Universität