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stat.ML2024
Boosting Causal Additive Models
Maximilian Kertel, Nadja Klein
We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspects of determining the causal or…
stat.ML2022
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Maximilian Kertel, Stefan Harmeling, Markus Pauly
Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process c…
stat.ML2022★ 1 cited
Estimating Gaussian Copulas with Missing Data
Maximilian Kertel, Markus Pauly
In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula m…