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Stable differentiable causal discovery for interventional data.

Project description

SDCD: Stable Differentiable Causal Discovery

SDCD is a method for inferring causal graphs from labeled interventional data.
You can read the associated preprint, "Stable Differentiable Causal Discovery", on arXiv.

sdci-cartoon

If you find this work useful, please consider citing our work:

@article{nazaret2023stable,
  title={Stable Differentiable Causal Discovery},
  author={Achille Nazaret and Justin Hong and Elham Azizi and David Blei},
  journal={arXiv preprint arXiv:2311.10263},
  year={2023}
}

Quick Start

You can install the package via pip install sdcd.

For the main implementation of the method, see the SDCD class.

For a tutorial on the basic usage of SDCD, see this notebook.

Code used to generate paper figures can be found in this folder.

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