3 citations · 3 across the 3 of their papers we have counts for
5 papers
Ancestor regression in structural vector autoregressive models
Christoph Schultheiss, Markus Ulmer, Peter Bühlmann
We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series wh…
Assessing the overall and partial causal well-specification of nonlinear additive noise models
Christoph Schultheiss, Peter Bühlmann
We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we…
On the pitfalls of Gaussian likelihood scoring for causal discovery
Christoph Schultheiss, Peter Bühlmann
We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecific…
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Alexander Immer, Christoph Schultheiss, Julia E. Vogt +3
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect can be written as a function of the cause and a noise source independe…
Multicarving for high-dimensional post-selection inference
Christoph Schultheiss, Claude Renaux, Peter Bühlmann
We consider post-selection inference for high-dimensional (generalized) linear models. Data carving (Fithian et al., 2014) is a promising technique to perform this task. However, i…