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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.

Metadata

Release files for sdcd 0.1.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Table of built distributions (wheels) for sdcd 0.1.4
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sdcd-0.1.4-py3-none-any.whl Python 3 none any Details

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