Zobrazeno 1 - 10
of 239
pro vyhledávání: '"Runge Jakob"'
Publikováno v:
Journal of Causal Inference, Vol 12, Iss 1, Pp 278-85 (2024)
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models. In this work, we focus on widely employed assumptions for causal disc
Externí odkaz:
https://doaj.org/article/cd654148a66d44148aba5b1f410cc0b3
Causal structure learning with data from multiple contexts carries both opportunities and challenges. Opportunities arise from considering shared and context-specific causal graphs enabling to generalize and transfer causal knowledge across contexts.
Externí odkaz:
http://arxiv.org/abs/2410.20405
Autor:
Brouillard, Philippe, Lachapelle, Sébastien, Kaltenborn, Julia, Gurwicz, Yaniv, Sridhar, Dhanya, Drouin, Alexandre, Nowack, Peer, Runge, Jakob, Rolnick, David
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the glob
Externí odkaz:
http://arxiv.org/abs/2410.07013
Structural vector autoregressive (SVAR) processes are commonly used time series models to identify and quantify causal interactions between dynamically interacting processes from observational data. The causal relationships between these processes ca
Externí odkaz:
http://arxiv.org/abs/2406.18191
A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over a discrete set of time points and between which there may be lagged and instantaneous effects. The qualitative causal structure of an SVAR proce
Externí odkaz:
http://arxiv.org/abs/2406.17422
Autor:
Wahl, Jonas, Runge, Jakob
Assessing the accuracy of the output of causal discovery algorithms is crucial in developing and comparing novel methods. Common evaluation metrics such as the structural Hamming distance are useful for assessing individual links of causal graphs. Ho
Externí odkaz:
http://arxiv.org/abs/2402.04952
In causal models, a given mechanism is assumed to be invariant to changes of other mechanisms. While this principle has been utilized for inference in settings where the causal variables are observed, theoretical insights when the variables of intere
Externí odkaz:
http://arxiv.org/abs/2312.03580
Autor:
Kaltenborn, Julia, Lange, Charlotte E. E., Ramesh, Venkatesh, Brouillard, Philippe, Gurwicz, Yaniv, Nagda, Chandni, Runge, Jakob, Nowack, Peer, Rolnick, David
Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest in supporting climate scientists' efforts on various tasks such as cl
Externí odkaz:
http://arxiv.org/abs/2311.03721
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained. As such, recent works to address this problem focus on various assumptions that
Externí odkaz:
http://arxiv.org/abs/2311.02695
Conditional independence testing (CIT) is a common task in machine learning, e.g., for variable selection, and a main component of constraint-based causal discovery. While most current CIT approaches assume that all variables are numerical or all var
Externí odkaz:
http://arxiv.org/abs/2310.11132