714 citations · 1k across the 33 of their papers we have counts for
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Partial Homoscedasticity in Causal Discovery with Linear Models
Jun Wu, Mathias Drton
Recursive linear structural equation models and the associated directed acyclic graphs (DAGs) play an important role in causal discovery. The classic identifiability result for thi…
Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids
Mathias Drton, Benjamin Hollering, Jun Wu
We consider structural equation models (SEMs), in which every variable is a function of a subset of the other variables and a stochastic error. Each such SEM is naturally associate…
Probability Metrics for Tropical Spaces of Different Dimensions
Roan Talbut, Daniele Tramontano, Yueqi Cao +2
The problem of comparing probability distributions is at the heart of many tasks in statistics and machine learning. Established comparison methods treat the standard setting that…
: Generating Realistic Production Data for Benchmarking Causal Discovery
Konstantin Göbler, Tobias Windisch, Mathias Drton +3
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a…
Confidence Sets for Causal Orderings
Y. Samuel Wang, Mladen Kolar, Mathias Drton
Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal mo…
Discussion of "A note on universal inference" by Timmy Tse and Anthony Davison
Mathias Drton, Hongjian Shi, David Strieder
Invited discussion for Stat of "A note on universal inference" by Timmy Tse and Anthony Davison (2022)