We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of ``independence'' to justify the inclusion or exclusion of a causal relationship. We show theoretically and empirically that these proxies may be arbitrarily different than the actual causal strength. Juxtaposed to existing differentiable causal discovery algorithms, \textsc{Dagma-DCE} uses an interpretable measure of causal strength to define weighted adjacency matrices. In a number of simulated datasets, we show our method achieves state-of-the-art level performance. We additionally show that \textsc{Dagma-DCE} allows for principled thresholding and sparsity penalties by domain-experts. The code for our method is available open-source at https://github.com/DanWaxman/DAGMA-DCE, and can easily be adapted to arbitrary differentiable models.

翻译：我们提出Dagma-DCE，一种面向可微分因果发现的可解释且模型无关的方案。当前可微分因果发现中的非参数或过参数方法使用模糊的"独立性"代理指标来证明因果关系包含或排除的合理性。我们从理论和实证两方面表明，这些代理指标可能与实际因果强度存在任意差异。与现有可微分因果发现算法相比，\textsc{Dagma-DCE}采用可解释的因果强度度量定义加权邻接矩阵。在多个模拟数据集上，我们证明该方法达到了最先进的性能水平。此外，我们表明\textsc{Dagma-DCE}支持领域专家进行原则性阈值设定和稀疏性惩罚。我们方法的代码已开源发布于https://github.com/DanWaxman/DAGMA-DCE，并可轻松适配任意可微分模型。