6 citations · 16 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
Anton Rask Lundborg, Rajen D. Shah, Jonas Peters
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalise…