  Causal modeling provides us with powerful counterfactual reasoning and
interventional mechanism to generate predictions and reason under various
what-if scenarios. However, causal discovery using observation data remains a
nontrivial task due to unobserved confounding factors, finite sampling, and
changes in the data distribution. These can lead to spurious cause-effect
relationships. To mitigate these challenges in practice, researchers augment
causal learning with known causal relations. The goal of the paper is to study
the impact of expert knowledge on causal relations in the form of additional
constraints used in the formulation of the nonparametric NOTEARS. We provide a
comprehensive set of comparative analyses of biasing the model using different
types of knowledge. We found that (i) knowledge that corrects the mistakes of
the NOTEARS model can lead to statistically significant improvements, (ii)
constraints on active edges have a larger positive impact on causal discovery
than inactive edges, and surprisingly, (iii) the induced knowledge does not
correct on average more incorrect active and/or inactive edges than expected.
We also demonstrate the behavior of the model and the effectiveness of domain
knowledge on a real-world dataset.
