no code implementations • 25 Oct 2023 • Christoph Schultheiss, Peter Bühlmann
We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models.
1 code implementation • 13 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., $Y = f(X) + g(X)N$.