Marloes H. Maathuis

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Maathuis

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Marloes H.

ORCID

0000-0002-3398-9893

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Publications 1 - 10 of 86

On efficient adjustment in causal graphs
Witte, Janine; Henckel, Leonard; Maathuis, Marloes H.; et al. (2020)
Journal of Machine Learning Research
We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustment sets are, however, not unique. Recent research has introduced a graphical criterion for an 'optimal' valid adjustment set (O-set). For a given graph, adjustment by the O-set yields the smallest asymptotic variance compared to other adjustment sets in certain parametric and non-parametric models. In this paper, we provide three new results on the O-set. First, we give a novel, more intuitive graphical characterisation: We show that the O-set is the parent set of the outcome node(s) in a suitable latent projection graph, which we call the forbidden projection. An important property is that the forbidden projection preserves all information relevant to total causal effect estimation via covariate adjustment, making it a useful methodological tool in its own right. Second, we extend the existing IDA algorithm to use the O-set, and argue that the algorithm remains semi-local. This is implemented in the R-package pcalg. Third, we present assumptions under which the O-set can be viewed as the target set of popular non-graphical variable selection algorithms such as stepwise backward selection.
Asymptotic optimality of the Westfall–Young permutation procedure for multiple testing under dependence
Meinshausen, Nicolai; Maathuis, Marloes H.; Bühlmann, Peter (2011)
The Annals of Statistics
Statistical estimation with censored data
Maathuis, Marloes H. (2008)
Einführungsvorlesung von Professorinnen und Professoren / ETH Zürich. Departement Mathematik
Distributional equivalence and structure learning for bow-free acyclic path diagrams
Bühlmann, Peter; Maathuis, Marloes H.; Evans, Robin J.; et al. (2017)
Electronic Journal of Statistics
Quantifying identifiability in independent component analysis
Sokol, Alexander; Maathuis, Marloes H.; Falkeborge, Benjamin (2014)
Electronic Journal of Statistics
Learning gene regulatory networks
Maathuis, Marloes H. (2013)
Simultaneous false discovery proportion bounds via knockoffs and closed testing
Li, Jinzhou; Maathuis, Marloes H.; Goeman, Jelle J. (2024)
Journal of the Royal Statistical Society Series B: Statistical Methodology
We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's k-familywise error rate control method and interpolation. We then generalize it by considering a collection of k values, and show that the bound of Katsevich and Ramdas is a special case of this method and can be uniformly improved. Next, we further generalize the method by using closed testing with a multi-weighted-sum local test statistic. This allows us to obtain a further uniform improvement and other generalizations over previous methods. We also develop an efficient shortcut for its implementation. We compare the performance of our proposed methods in simulations and apply them to a data set from the UK Biobank.
Causal structure learning and inference in high dimensional systems
Maathuis, Marloes H. (2013)
Order-independent constraint-based causal structure learning
Colombo, Diego; Maathuis, Marloes H. (2014)
Journal of Machine Learning Research
Graphical models and causality, part 2
Maathuis, Marloes H. (2014)

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Marloes H. Maathuis

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