reproducibilityindex.ai

Marrying Causal Representation Learning with Dynamical Systems for Science

Authors: Dingling Yao, Caroline Muller, Francesco Locatello

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	We experiment with a wind simulator with partially known factors of variation. We also apply the resulting model to real-world climate data and successfully answer downstream causal questions in line with existing literature on climate change.Code is available at https://github.com/Causal Learning AI/crl-dynamical-systems.
Researcher Affiliation	Academia	Dingling Yao, Caroline Muller, and Francesco Locatello Institute of Science and Technology Austria
Pseudocode	No	The paper describes its methods using text and mathematical equations but does not include any explicit pseudocode blocks or algorithm listings.
Open Source Code	Yes	Code is available at https://github.com/Causal Learning AI/crl-dynamical-systems.
Open Datasets	Yes	We evaluate the models on sea surface temperature dataset SST-V2 [21].
Dataset Splits	Yes	We chunk the time series into slices of 4 years in training while keeping last four years as out-of-distribution forecasting task.
Hardware Specification	Yes	All 12 jobs ran with 24GB of RAM, 8 CPU cores, and a single node GPU, which is, in most cases, NVIDIA Ge Force RTX2080Ti.
Software Dependencies	No	The paper mentions software like the 'Speedy Weather Julia package' and 'scikit-learn' but does not provide specific version numbers for these or other software dependencies.
Experiment Setup	Yes	Table 3: Training setup for wind simulation in 6.2.