2 citations · 4 across the 4 of their papers we have counts for
4 papers
A Consistent Estimator for Confounding Strength
Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar +1
Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it…
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig +1
Full Bayesian posteriors are rarely analytically tractable, which is why real-world Bayesian inference heavily relies on approximate techniques. Approximations generally differ fro…
Interpolation and Regularization for Causal Learning
Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg +1
We study the problem of learning causal models from observational data through the lens of interpolation and its counterpart -- regularization. A large volume of recent theoretical…
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees
Solveig Klepper, Christian Elbracht, Diego Fioravanti +4
Originally, tangles were invented as an abstract tool in mathematical graph theory to prove the famous graph minor theorem. In this paper, we showcase the practical potential of ta…