Stable Differentiable Causal Discovery
by Achille Nazaret, Justin Hong, Elham Azizi, David Blei
Abstract:
Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization. But existing DCD methods are numerically unstable, with poor performance beyond tens of variables. In this paper, we propose Stable Differentiable Causal Discovery (SDCD), a new method that improves previous DCD methods in two ways: (1) It employs an alternative constraint for acyclicity; this constraint is more stable, both theoretically and empirically, and fast to compute. (2) It uses a training procedure tailored for sparse causal graphs, which are common in real-world scenarios. We first derive SDCD and prove its stability and correctness. We then evaluate it with both observational and interventional data and on both small-scale and large-scale settings. We find that SDCD outperforms existing methods in both convergence speed and accuracy and can scale to thousands of variables.
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Github Repo __
@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} }