  Recent arguments that machine learning (ML) is facing a reproducibility and
replication crisis suggest that some published claims in ML research cannot be
taken at face value. These concerns inspire analogies to the replication crisis
affecting the social and medical sciences. They also inspire calls for the
integration of statistical approaches to causal inference and predictive
modeling. A deeper understanding of what reproducibility concerns in supervised
ML research have in common with the replication crisis in experimental science
puts the new concerns in perspective, and helps researchers avoid "the worst of
both worlds," where ML researchers begin borrowing methodologies from
explanatory modeling without understanding their limitations and vice versa. We
contribute a comparative analysis of concerns about inductive learning that
arise in causal attribution as exemplified in psychology versus predictive
modeling as exemplified in ML. We identify themes that re-occur in reform
discussions, like overreliance on asymptotic theory and non-credible beliefs
about real-world data generating processes. We argue that in both fields,
claims from learning are implied to generalize outside the specific environment
studied (e.g., the input dataset or subject sample, modeling implementation,
etc.) but are often impossible to refute due to undisclosed sources of variance
in the learning pipeline. In particular, errors being acknowledged in ML expose
cracks in long-held beliefs that optimizing predictive accuracy using huge
datasets absolves one from having to consider a true data generating process or
formally represent uncertainty in performance claims. We conclude by discussing
risks that arise when sources of errors are misdiagnosed and the need to
acknowledge the role of human inductive biases in learning and reform.
