CauseMe A platform to benchmark causal inference methods


Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain. Interactions in such high-dimensional dynamical systems often involve time-delays, nonlinearity, and strong autocorrelations. These present major challenges for causal discovery techniques such as the traditional Granger causality. Further method development and comparison requires datasets with known causal ground truth. Our platform extends previous causality challenges as listed below under Links.

The CauseMe platform provides such benchmark datasets generated from synthetic models mimicking real data challenges as well as real data sets where the causal structure is known with high confidence. This allows to assess the performance of causal inference methods in time series problems and help choose the right method for a particular problem. We believe that data sharing and reproducability will greatly advance progress on method development. The available benchmark datasets vary in dimensionality, complexity and sophistication. The aim is to assess methods capabilities under a common experimental framework.

There are two ways to contribute:

  1. Predicting causal connections from available datasets.
  2. Uploading multivariate time series data with known causal ground truth.

The datasets are released to the scientific community for analysis and experimentation. Method developers can upload their predictions (matrices of causal connections) and the platform evaluates and ranks the methods according to different metrics of performance. CauseMe currently contains several different datasets, but it is ready to scale up to many more! We encourage contributions from applied sciences and practitioners in many areas. Please contact us if you want to contribute!

Cause Me!


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