| Summary: | We introduce <sc>Dagma-DCE</sc>, 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 with existing differentiable causal discovery algorithms, <sc>Dagma-DCE</sc> 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 <sc>Dagma-DCE</sc> allows for principled thresholding and sparsity penalties by domain-experts. The code for our method is available open-source at <uri>https://github.com/DanWaxman/DAGMA-DCE</uri>, and can easily be adapted to arbitrary differentiable models.
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