Nikolaj Thams | DeepAI

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∙ 05/31/2022

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

We give a method for proactively identifying small, plausible shifts in ...

0 Nikolaj Thams, et al. ∙

∙ 03/11/2022

Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past

Instrumental variable (IV) regression relies on instruments to infer cau...

0 Nikolaj Thams, et al. ∙

∙ 02/02/2022

Invariant Ancestry Search

Recently, methods have been proposed that exploit the invariance of pred...

0 Phillip B Mogensen, et al. ∙

∙ 10/25/2021

Local Independence Testing for Point Processes

Constraint based causal structure learning for point processes require e...

0 Nikolaj Thams, et al. ∙

∙ 06/01/2021

Invariant Policy Learning: A Causal Perspective

In the past decade, contextual bandit and reinforcement learning algorit...

0 Sorawit Saengkyongam, et al. ∙

∙ 05/22/2021

Statistical Testing under Distributional Shifts

Statistical hypothesis testing is a central problem in empirical inferen...

0 Nikolaj Thams, et al. ∙

∙ 03/03/2021

Regularizing towards Causal Invariance: Linear Models with Proxies

We propose a method for learning linear models whose predictive performa...

0 Michael Oberst, et al. ∙

∙ 02/21/2020

Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values

In this article, we describe the algorithms for causal structure learnin...

0 Sebastian Weichwald, et al. ∙

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