Zobrazeno 1 - 10
of 238
pro vyhledávání: '"Runge, Jakob"'
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
Many state-of-the-art causal discovery methods aim to generate an output graph that encodes the graphical separation and connection statements of the causal graph that underlies the data-generating process. In this work, we argue that an evaluation o
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
In recent years, a growing number of method and application works have adapted and applied the causal-graphical-model framework to time series data. Many of these works employ time-resolved causal graphs that extend infinitely into the past and futur
Externí odkaz:
http://arxiv.org/abs/2310.05526
Autor:
Wagner, Felix, Nachtigall, Florian, Franken, Lukas, Milojevic-Dupont, Nikola, Pereira, Rafael H. M., Koch, Nicolas, Runge, Jakob, Gonzalez, Marta, Creutzig, Felix
Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car trav
Externí odkaz:
http://arxiv.org/abs/2308.16599
Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time series of river runoff from different catchments. The local catchment sys
Externí odkaz:
http://arxiv.org/abs/2306.12896