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Neural Time-Invariant Causal Discovery from Time Series Data

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NTiCD

This repository is the implementation of the paper: Saima Absar, Yongkai Wu, and Lu Zhang. “Neural Time-invariant Causal Discovery from Time Series Data." International Joint Conference on Neural Networks. Gold Coast, Queensland, Australia. 2023.

Overview

  • ./singleseq: contains the codes for experiments with single-sequence synthetic data, including sample data and data-generation code
  • ./multiseq: contains the codes for experiments with multi-sequence synthetic data, including sample data and data-generation code
  • ./realData: contains the codes for experiments with Netsim data, including data and data-preparation code

Usage

To reproduce the experiments, get into the corresponding directory and run the following commands:

  • bash run_singleseq.sh
  • bash run_multiseq.sh
  • bash run_netsim.sh

Reference

Please cite the following paper if using this code. NTiCD

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Neural Time-Invariant Causal Discovery from Time Series Data

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