CAUSAL INFERENCE: METHOD FOR ATTRIBUTION
We contributed to the continuous development of the tigramite (https://github.com/jakobrunge/tigramite/) Python package for causal inference methods.
This package provides a wide range of constraint-based causal discovery and causal effect estimation methods.
Description
A particular focus in the XAIDA-related developments was the implementation of mixed-type non-parametric conditional independence tests applicable also to synergistic problems, non-stationary causal discovery methods, and non-parametric causal mediation methods for the estimation of direct and indirect effects in mixed-type data; mediation analysis can also inform about which pathways a particular effect should be attributed to.
Potential Users
- Researchers, in applied packages of XAIDA or otherwise concerned with causal relations in climate (or similar) data.
- Technically interested planners or engineers who wish to quantify the effect of interventions or supplement their causal understanding of a problem.
Guide: An extensive guide and many tutorials can be found in the GitHub repository https://github.com/jakobrunge/tigramite/.
Availability: The tool is a pip-installable Python package. It can be used with basic knowledge of the Python programming language.
Use Cases: For a list of in-depth tutorials as well as use cases see https://github.com/jakobrunge/tigramite/tree/master/tutorials/.
References
- Detecting causal associations in large nonlinear time series datasets. J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic. Science Advances 5(11): eaau4996 (2019).
- Causal network reconstruction from time series: From theoretical assumptions to practical estimation. J. Runge. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28:7 (2018).
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003469.
Tools
Ensemble boosting uses climate models to efficiently generate very intense and rare weather and climate extremes that can be analyzed for planning and stress testing of critical infrastructure.
Stochastic Weather Generation is a computationally light tool to simulate temperatures for worse case heat extremes, at city up to country level.
In the aftermath of an extreme weather event, the event is studied more and more often by an operational attribution service. Two examples of such operational attribution services are used within XAIDA. These operational services generally answer slightly different questions and are complementary.
ClimaMeter is an international consortium designed to
Storyline is a methodology to determine when in the future extreme heat events above a chosen threshold become likely in cities, and to present the meteorological conditions that lead up to it.
In the aftermath of an extreme weather event, the event is studied more and more often by an operational attribution service. Two examples of such operational attribution services are used within XAIDA. These operational services generally answer slightly different questions and are complementary.
World Weather Attribution (WWA), an initiative founded
Importance sampling is a way to preferentially select out of a range of model simulations the ones that will lead to extremes of the metric of interest in an early stage of simulation, thus increasing computing efficiency for the cases of interest.
We contributed to the continuous development of the tigramite (https://github.com/jakobrunge/tigramite/) Python package for causal inference methods.
This package provides a wide range of constraint-based causal discovery and causal effect estimation methods.
GreenEarthNet is a machine learning-powered toolkit for predicting ecosystem responses to climate changes by leveraging Earth observation data and climate models. It supports effective environmental monitoring and forecasting for improved climate action.
The Artificial Intelligence for Disentangling Extremes (AIDE) toolbox allows for tackling generic problems of detection and impact assessment of events such as tropical cyclones and severe convective storms, heat waves, and droughts, as well as persistent winter extremes, among others
CAUSEME web-platform is a platform to benchmark causal discovery methods based on ground truth benchmark datasets featuring different real data challenges.