This repository contains code for our work on estimating causal influence among subsystems.

• The work on causal inference within groups can be cited using below citation information.

@article{ahmad2024deep,
  title={Deep Learning-based Group Causal Inference in Multivariate Time-series},
  author={Ahmad, Wasim and Shadaydeh, Maha and Denzler, Joachim},
  journal={arXiv preprint arXiv:2401.08386},
  year={2024}
}

We discover full causal graph in multivariate nonlinear systems by testing model invariance against Knockoffs-based interventional environments:

1. First we train deep network $f_i$ using data from observational environment $E_i$.
2. Then we expose the model to Knockoffs-based interventional environments $E_k$.
3. For each pair variables ${z_i, z_j}$ in nonlinear system, we test model invariance across environments.
4. We perform KS test over distribution $R_i$, $R_k$ of model residuals in various environments. Our NULL hypothesis is that variable $z_i$ does not cause $z_j$, $H_0$: $R_i$ = $R_k$, else the alternate hypothesis $H_1$: $R_i$ != $R_k$ is accepted.

We test our method on synthetic as well as real data which can be found under datasets/ directory. The synthetic data is generated using file src/synthetic_dataset.py.

src/main.py is our main file, where we model multivariate non-linear data using deep networks.

• src/deepcause.py for actual and counterfactual outcome generation using interventions.
• src/preprocessing.py for data loading and preprocessing.
• src/knockoffs.py generate knockoffs of the original variables.
• src/daignostics.py to determine the goodness of the generated knockoff copies.
• DeepKnockoffs/ contains the knockoffs generation methods.
• datasets/ contains the generated synthetic data and real dataset.
• model/ contains trained models that we used for different datasets.

requirement.yml contains all the packages that are related to the project. To install them, simply create a new conda environment and type

conda env create -f environment.yml

This work is funded by the Carl Zeiss Foundation within the scope of the program line "Breakthroughs: Exploring Intelligent Systems" for "Digitization � explore the basics, use applications" and the DFG grant SH 1682/1-1.