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We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causalâ€¦ (More)

We assume that we have observational data, generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible toâ€¦ (More)

The pcalg package for R (R Development Core Team (2010)) can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In thisâ€¦ (More)

- Diego Colombo, Marloes H. Maathuis
- Journal of Machine Learning Research
- 2014

We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCIand CCDalgorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen etâ€¦ (More)

- Michael G. Hudgens, Marloes H. Maathuis, Peter B. Gilbert
- Biometrics
- 2007

This article considers three nonparametric estimators of the joint distribution function for a survival time and a continuous mark variable when the survival time is interval censored and the markâ€¦ (More)

- Marloes H. Maathuis, Diego Colombo, Markus Kalisch, Peter BÃ¼hlmann
- Nature Methods
- 2010

editorial office: 75 Varick Street, Fl 9, New York, NY 10013-1917. Tel (212) 726 9200, Fax: (212) 689 9702. annual subscription rates: USA/ Canada: US$150 (personal), US$2,513 (institution), Canadaâ€¦ (More)

- Marloes H. Maathuis, Diego Colombo
- ArXiv
- 2013

We generalize Pearlâ€™s back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hiddenâ€¦ (More)

- Piet Groeneboom, Marloes H. Maathuis, Jon A. Wellner
- Annals of statistics
- 2008

We study nonparametric estimation for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider aâ€¦ (More)

We study computational aspects of the nonparametric maximum likelihood estimator (NPMLE) for the distribution function of bivariate interval censored data. The computation of the NPMLE consists ofâ€¦ (More)

Delft University of Technology and Vrije Universiteit Amsterdam, University of Washington and University of Washington We study nonparametric estimation of the sub-distribution functions for currentâ€¦ (More)