Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Schultheiss, Christoph"'
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 while retaining its favorable properties, i.e., explicit error control f
Externí odkaz:
http://arxiv.org/abs/2403.03778
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 can infer the causal effect even in cases of such misspecification. W
Externí odkaz:
http://arxiv.org/abs/2310.16502
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 misspecification in terms of non-Gaussian error distribution. We present a surpri
Externí odkaz:
http://arxiv.org/abs/2210.11104
Autor:
Immer, Alexander, Schultheiss, Christoph, Vogt, Julia E., Schölkopf, Bernhard, Bühlmann, Peter, Marx, Alexander
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 cau
Externí odkaz:
http://arxiv.org/abs/2210.09054
We present a new method for causal discovery in linear structural equation models. We propose a simple ``trick'' based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally,
Externí odkaz:
http://arxiv.org/abs/2205.08925
We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present
Externí odkaz:
http://arxiv.org/abs/2109.14544
Autor:
Padoan, Benedetta, Casar, Christian, Krause, Jenny, Schultheiss, Christoph, Baumdick, Martin E., Niehrs, Annika, Zecher, Britta F., Pujantell, Maria, Yuki, Yuko, Martin, Maureen, Remmerswaal, Ester B.M., Dekker, Tamara, van der Bom-Baylon, Nelly D., Noble, Janelle A., Carrington, Mary, Bemelman, Frederike J., van Lier, Rene A.W., Binder, Mascha, Gagliani, Nicola, Bunders, Madeleine J., Altfeld, Marcus
Publikováno v:
In Cell Reports 23 April 2024 43(4)
Autor:
Thiele, Benjamin, Stein, Alexander, Schultheiß, Christoph, Paschold, Lisa, Jonas, Hanna, Goekkurt, Eray, Rüssel, Jörn, Schuch, Gunter, Wierecky, Jan, Sinn, Marianne, Tintelnot, Joseph, Petersen, Cordula, Rothkamm, Kai, Vettorazzi, Eik, Binder, Mascha
Publikováno v:
In Clinical Colorectal Cancer June 2024
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, it suffers from the instability of the model selector and hence, may le
Externí odkaz:
http://arxiv.org/abs/2006.04613
Autor:
Schultheiss Christoph, Bühlmann Peter
Publikováno v:
Journal of Causal Inference, Vol 11, Iss 1, Pp 689-96 (2023)
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 misspecification in terms of non-Gaussian error distribution. We present a surpri
Externí odkaz:
https://doaj.org/article/efcd78425f114fb5b487e52415089751