7 citations · 17 across the 8 of their papers we have counts for
8 papers
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
Nikolaj Thams, Michael Oberst, David Sontag
We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. These shifts are defined via parametric c…
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald +1
Instrumental variable (IV) regression relies on instruments to infer causal effects from observational data with unobserved confounding. We consider IV regression in time series mo…
Invariant Ancestry Search
Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response var…
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…
Invariant Policy Learning: A Causal Perspective
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters +1
Contextual bandit and reinforcement learning algorithms have been successfully used in various interactive learning systems such as online advertising, recommender systems, and dyn…
Statistical Testing under Distributional Shifts
Nikolaj Thams, Sorawit Saengkyongam, Niklas Pfister +1
In this work, we introduce statistical testing under distributional shifts. We are interested in the hypothesis for a target distribution , but observe data from…
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…
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…