While interpretability often involves finding more parsimonious or sparser models to facilitate human understanding, Netflix also seeks to achieve human interpretability by pursuing causal learning. Predictive models can be impressively accurate in a passive setting but might disappoint a human user who expects the recovered relationships to be causal. More importantly, a predictive model's outcomes may no longer be accurate if the input variables are perturbed through an active intervention. I will briefly discuss applications at Netflix across messaging, marketing and originals promotion which leverage causal modeling in order to achieve models that can be actionable as well as interpretable. In particular, techniques such as two stage least squares (2SLS), instrumental variables (IV), extensions to generalized linear models (GLMs), and other causal methods will be summarized. These causal models can surprisingly recover more interpretable and simpler models than their purely predictive counterparts. Furthermore, sparsity can potentially emerge when causal models ignore spurious relationships that might otherwise be recovered in a purely predictive objective function. In general, causal models achieve better results algorithmically in active intervention settings and enjoy broader adoption from human stakeholders.