Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman Kurt Butler Petar M. Djuric
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.