cits 1.4

CITS algorithm for inferring causality from time series data

Python Package for CITS algorithm: Causal Inference from Time Series data

CITS algorithm infers causal relationships in time series data based on structural causal model and Markovian condition of arbitrary but finite order. See the paper for details.

Installation

You can get the latest version of CITS package as follows

pip install cits

Requirements

• Python >= 3.6
• R >= 4.0
• R package kpcalg and its dependencies. They can be installed in R or RStudio as follows:

> install.packages("BiocManager")
> BiocManager::install("graph")
> BiocManager::install("RBGL")
> install.packages("pcalg")
> install.packages("kpcalg")

Documentation

Documentation is available at readthedocs.org

Tutorial

Visit this Google Colab for getting started with this package.

Alternatively, see the Getting Started in the documentation.

Contributing

Your help is absolutely welcome! Please do reach out or create a future branch!

Citation

Biswas, R., Sripada, S., Mukherjee, S. & Abbasi-Asl, R. (2025) CITS: Nonparametric Statistical Causal Modeling for High-Resolution Neural Time Series. In Review. https://arxiv.org/abs/2508.01920