🎓 CS-673 Project (Intro to Deep Generative Models)

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📜 Overview

The task of Causal Discovery is to uncover the true DAG $\mathcal{G}$ given a dataset $D$ such that $p_D ∼ \mathcal{G}$. Since the work of [ZARX18], which transformed the task of causal discovery to a continuous optimization program with acyclicity constraints, significant attention has shifted to deep learning-based causal discovery algorithms.

In this project, we select DECI (Deep End-to-end Causal Inference), a SOTA causal discovery algorithm for iid observational data by [GAF+22], a variational inference model modeling exogenous noise as a normalizing flow.

Since the literature on causal discovery algorithms for time-series data using deep learning techniques is quite limited and virginal, we opt to implement DECI on time-series data using lagged cross-sectional data. Finally, we evaluate our performance against non-deep-learning-inspired algorithms (PCMCI, PCMCI(+) etc.) on time-series synthetic data with known ground truth causal graph [LKSS20].

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📚 References

• [GAF+22] Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, et al. Deep end-to-end causal inference. arXiv preprint arXiv:2202.02195, 2022.
• [LKSS20] Andrew R. Lawrence, Marcus Kaiser, Rui Sampaio, and Maksim Sipos. Data generating process to evaluate causal discovery techniques for time series data. Causal Discovery Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems, 2020.
• [VCB22] Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden. D’ya like DAGs? a survey on structure learning and causal discovery. ACM Computing Surveys, 55(4):1–36, 2022.
• [ZARX18] Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. Dags with NO-TEARS: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, 31, 2018.

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🛠️ Setup Instructions

There are two separate environments that need to be configured to reproduce this project: CDML and DECI (causica).

🐍 Creating Virtual Environments

You may create the virtual environments with their respective requirements using the provided .yml files, using for example your Anaconda installation, on your shell as

1. For CDML:

    conda env create -f environment-cdml.yml

2. For causica:

   conda env create -f environment-causica.yml

📌 Environment Details

• The first environment runs on Python 3.8.19.
• The second environment runs on Python 3.10.1, 🔥 PyTorch 1.13.0 and PyTorch lightning 2.2.2.

🥰 Reproducing the experiments

• Run generate_dataset.ipynb to generate a CDML configuration, plot the causal graph and generate the corresponding time-lagged dataset.

• Run experiments.ipynb to run a DECI model on a CDML configuration, compute the metrics and compare to the ground truth graph, as well as PCMCI.

• Run RunAll.ipynb to evaluate all pre-trained DECI models on each datasetavailable at the datasets folder and compare with PCMCI.

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Enjoy! 🚀