This repository contains the implementation of the work A Practical Approach to Causal Inference over Time, where we present a framework for estimating causal effects over time in dynamical systems. The proposed causal VAR framework allows us to perform causal inference over time from observational time series data.
Our experiments on synthetic and real-world datasets show that the proposed framework achieves strong performance in terms of observational forecasting while enabling accurate estimation of the causal effect of interventions on dynamical systems.
Use the following Jupyter notebooks to reproduce the results presented in the paper:
These notebooks showcase how to evaluate the causal effects of interventions over time using the provided datasets and models.
- Python >= 3.9
- Libraries listed in
requirements.txt.
Additional libraries might be required for optional functionalities.
Clone the repository and set up the environment:
git clone https://github.com/marti5ini/ci-over-time.git
cd ci-over-timeIt is recommended to use a virtual environment:
python3 -m venv ./venv # Optional but recommended
source ./venv/bin/activate
pip install -r requirements.txt
If you use our causal VAR framework in your research, please cite our paper:
@inproceedings{
cinquini2025ciovertime,
title={A Practical Approach to Causal Inference over Time},
author={Cinquini, Martina, Beretta Isacco, Ruggieri, Salvatore and Valera, Isabel},
booktitle={The 39th Annual AAAI Conference on Artificial Intelligence},
year={2025},
url={https://openreview.net/forum?id=2EBCWWS0Me}
}