14 citations · 53 across the 15 of their papers we have counts for
6 papers · 1 filter
Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Shan Zhao, Ilija Trajkovic, Julia Kaltenborn +4
Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models projections. However, the b…
Causal Climate Emulation with Bayesian Filtering
Sebastian Hickman, Ilija Trajkovic, Julia Kaltenborn +6
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally e…
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn +6
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 Niño…
Towards Causal Representations of Climate Model Data
Julien Boussard, Chandni Nagda, Julia Kaltenborn +5
Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions…
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh +6
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…
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz +1
We present a sound and complete algorithm, called iterative causal discovery (ICD), for recovering causal graphs in the presence of latent confounders and selection bias. ICD relie…