  Causal models and methods have great promise, but their progress has been
stalled. Proposals using causality get squeezed between two opposing
worldviews. Scientific perfectionism--an insistence on only using "correct"
models--slows the adoption of causal methods in knowledge generating
applications. Pushing in the opposite direction, the academic discipline of
computer science prefers algorithms with no or few assumptions, and
technologies based on automation and scalability are often selected for
economic and business applications. We argue that these system-centric
inductive biases should be replaced with a human-centric philosophy we refer to
as scientific pragmatism. The machine learning community must strike the right
balance to make space for the causal revolution to prosper.
