CAUSAL INFERENCE: METHOD FOR ATTRIBUTION
A platform to benchmark causal discovery methods based on ground truth benchmark datasets featuring different real data challenges
Description
CauseMe is a user-friendly online platform that aims to function as the link between causal inference experts and domain specialists. This platform empowers non-causality experts to employ data-driven causal discovery methods, facilitating data exploration and initial graph construction. It also provides ground-truth benchmark datasets featuring different real data challenges to assess and compare the performance of causal discovery methods.
Potential User Groups
- Experts less versed in causality but eager to conduct further data experiments.
- Researchers interested in performing simple forecasting models using the causal results obtained from the causal discovery methods.
- Versatile fields: Agriculture, humanitarian organizations, climate scientists, etc.
Availability and usage guide
CauseMe is a web service under development and publicly available at https://causeme.uv.es/. The website includes the section “How it works” with a video tutorial on how to use it, as well as some examples.
Use Cases
- Causal discovery to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023 (Causal discovery reveals complex patterns of drought-induced displacement. J.M. Tárraga, E. Sevillano-Marco, J. Muñoz-Marí, M. Piles, V. Sitokonstantinou, M. Ronco, M. T. Miranda, J. Cerdà, G. Camps-Valls. iScience 27:9 (2024)).
- Causal Machine Learning for Sustainable Agroecosystems (Causal Machine Learning for Sustainable Agroecosystems. V. Sitokonstantinou, E. Díaz, J. Cerdà, M. Piles, I. Athanasiadis, H. Kerner, G. Martini, L. Sweet, I. Tsoumas, J. Zscheischler, G. Camps-Valls. Preprint (2024))
References
Inferring causation from time series in Earth system sciences. J. Runge, S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M.D. Mahecha, J. Munoz-Mari, E.H. van Ness, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B. Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, J. Zscheischler. Nature Communications 10: 2553 (2019).
CauseMe: An online system for benchmarking causal discovery methods. J. Muñoz-Marí, G. Mateo, J. Runge, and G. Camps-Valls. In preparation (2024)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003469.
Tools
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.