Search Results for author: Christoph Schultheiss

Found 2 papers, 1 papers with code

Assessing the overall and partial causal well-specification of nonlinear additive noise models

no code implementations25 Oct 2023 Christoph Schultheiss, Peter Bühlmann

We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models.

On the Identifiability and Estimation of Causal Location-Scale Noise Models

1 code implementation13 Oct 2022 Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i. e., $Y = f(X) + g(X)N$.

Causal Discovery Causal Inference

Cannot find the paper you are looking for? You can Submit a new open access paper.