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  1. Article

    Open Access

    Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD

    Causal discovery is an increasingly important method for data analysis in the field of medical research. In this paper, we consider two challenges in causal discovery that occur very often when working with me...

    Elena Sokolova, Daniel von Rhein in International Journal of Data Science and … (2017)

  2. Chapter and Conference Paper

    Copula PC Algorithm for Causal Discovery from Mixed Data

    We propose the ‘Copula PC’ algorithm for causal discovery from a combination of continuous and discrete data, assumed to be drawn from a Gaussian copula model. It is based on a two-step approach. The first ste...

    Ruifei Cui, Perry Groot in Machine Learning and Knowledge Discovery in Databases (2016)

  3. No Access

    Chapter and Conference Paper

    Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data

    Causal discovery is an increasingly popular method for data analysis in the field of medical research. In this paper we consider two challenges in causal discovery that occur very often when working with medic...

    Elena Sokolova, Perry Groot, Tom Claassen in Artificial Intelligence in Medicine (2015)

  4. No Access

    Chapter and Conference Paper

    Causal Discovery from Databases with Discrete and Continuous Variables

    Bayesian Constraint-based Causal Discovery (BCCD) is a state-of-the-art method for robust causal discovery in the presence of latent variables. It combines probabilistic estimation of Bayesian networks over su...

    Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes in Probabilistic Graphical Models (2014)