3 papers
stat.ME2024
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…
stat.ME2023
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…
stat.ME2020
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…