The methods developed in the research theme on causality are continuously added to the Tigramite package. Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Tigramite also includes functions for high-quality plots of the results.
The package is hosted on Github: https://github.com/jakobrunge/tigramite
Tigramite 4.0.0.-beta was just released which includes mostly improvements under the hood, but also some API changes. The most recent version is described in the paper
Jakob Runge, Peer Nowack, Marlene Kretschmer, Seth Flaxman, and Dino Sejdinovic. 2018. “Detecting Causal Associations in Large Nonlinear Time Series Datasets.” https://arxiv.org/abs/1702.07007v2