9 citations · 21 across the 4 of their papers we have counts for
6 papers
Causality and Generalizability: Identifiability and Learning Methods
Martin Emil Jakobsen
This PhD thesis contains several contributions to the field of statistical causal modeling. Statistical causal models are statistical models embedded with causal assumptions that a…
Structure Learning for Directed Trees
Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann +1
Knowing the causal structure of a system is of fundamental interest in many areas of science and can aid the design of prediction algorithms that work well under manipulations to t…
A causal framework for distribution generalization
Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen +2
We consider the problem of predicting a response from a set of covariates when test and training distributions differ. Since such differences may have causal explanations,…
Distributional robustness of K-class estimators and the PULSE
Martin Emil Jakobsen, Jonas Peters
While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded. We prove that…
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen +3
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information…
Distance Covariance in Metric Spaces: Non-Parametric Independence Testing in Metric Spaces (Master's thesis)
Martin Emil Jakobsen
The aim of this thesis is to find a solution to the non-parametric independence problem in separable metric spaces. Suppose we are given finite collection of samples from an i.i.d.…