Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Maathuis Marloes H"'
This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. In particular, we aim to identify the intervened variable in
Externí odkaz:
http://arxiv.org/abs/2410.12151
We propose an easy-to-use adjustment estimator for the effect of a treatment based on observational data from a single (social) network of units. The approach allows for interactions among units within the network, called interference, and for observ
Externí odkaz:
http://arxiv.org/abs/2312.02717
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
Externí odkaz:
http://arxiv.org/abs/2212.12822
We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage least squares estimator in the setting of a linear structural equation
Externí odkaz:
http://arxiv.org/abs/2208.03697
Causal inference for extreme events has many potential applications in fields such as climate science, medicine and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing metho
Externí odkaz:
http://arxiv.org/abs/2110.06627
Autor:
Hangartner, Dominik, Marbach, Moritz, Henckel, Leonard, Maathuis, Marloes H., Kelz, Rachel R., Keele, Luke
Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. IV analyses can be used both to analyze randomized trials with noncompliance and as a form of natural experiment. In these analyses, investigators o
Externí odkaz:
http://arxiv.org/abs/2103.06328
Recent years have seen many advances in methods for causal structure learning from data. The empirical assessment of such methods, however, is much less developed. Motivated by this gap, we pose the following question: how can one assess, in a given
Externí odkaz:
http://arxiv.org/abs/2006.15387
Publikováno v:
Journal of Machine Learning Research 21(246):1-45 (2020)
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,
Externí odkaz:
http://arxiv.org/abs/2002.06825
Autor:
Maathuis Marloes H, Grill Eva, Fellinghauer Bernd AG, Kalisch Markus, Mansmann Ulrich, Bühlmann Peter, Stucki Gerold
Publikováno v:
BMC Medical Research Methodology, Vol 10, Iss 1, p 14 (2010)
Abstract Background Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disabil
Externí odkaz:
https://doaj.org/article/a2c64a9f50ce44d2b605d65436d3e5e4
Autor:
Li, Jinzhou, Maathuis, Marloes H.
We propose a new method to learn the structure of a Gaussian graphical model with finite sample false discovery rate control. Our method builds on the knockoff framework of Barber and Cand\`{e}s for linear models. We extend their approach to the grap
Externí odkaz:
http://arxiv.org/abs/1908.11611