329 citations · 1.2k across the 27 of their papers we have counts for
11 papers · 1 filter
On efficient adjustment in causal graphs
Janine Witte, Leonard Henckel, Marloes H. Maathuis +1
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 kn…
Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models
Leonard Henckel, Emilija Perković, Marloes H. Maathuis
Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, th…
Interpreting and using CPDAGs with background knowledge
Emilija Perković, Markus Kalisch, Maloes H. Maathuis
We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restrictions on a Markov…
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
Emilija Perković, Johannes Textor, Markus Kalisch +1
We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic graphs (DAGs), maximum…
A Complete Generalized Adjustment Criterion
Emilija Perković, Johannes Textor, Markus Kalisch +1
Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify…
High-dimensional consistency in score-based and hybrid structure learning
Preetam Nandy, Alain Hauser, Marloes H. Maathuis
Main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for…
Quantifying identifiability in independent component analysis
Alexander Sokol, Marloes H. Maathuis, Benjamin Falkeborg
We are interested in consistent estimation of the mixing matrix in the ICA model, when the error distribution is close to (but different from) Gaussian. In particular, we consider…
Asymptotic optimality of the Westfall--Young permutation procedure for multiple testing under dependence
Nicolai Meinshausen, Marloes H. Maathuis, Peter Bühlmann
Test statistics are often strongly dependent in large-scale multiple testing applications. Most corrections for multiplicity are unduly conservative for correlated test statistics,…
Current status data with competing risks: Limiting distribution of the MLE
Piet Groeneboom, Marloes H. Maathuis, Jon A. Wellner
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…
Current status data with competing risks: Consistency and rates of Convergence of the MLE
Piet Groeneboom, Marloes H. Maathuis, Jon A. Wellner
We study nonparametric estimation of the sub-distribution functions for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estim…
Inconsistency of the MLE for the joint distribution of interval censored survival times and continuous marks
Marloes H. Maathuis, Jon A. Wellner
This paper considers the nonparametric maximum likelihood estimator (MLE) for the joint distribution function of an interval censored survival time and a continuous mark variable.…