3 papers
stat.ME2021
Local Independence Testing for Point Processes
Nikolaj Thams, Niels Richard Hansen
Constraint based causal structure learning for point processes require empirical tests of local independence. Existing tests require strong model assumptions, e.g. that the true da…
cs.LG2021
Regularizing towards Causal Invariance: Linear Models with Proxies
Michael Oberst, Nikolaj Thams, Jonas Peters +1
We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, when noisy proxies of those variables are ava…
stat.ML2020
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…