  Causality is a fundamental part of the scientific endeavour to understand the
world. Unfortunately, causality is still taboo in much of psychology and social
science. Motivated by a growing number of recommendations for the importance of
adopting causal approaches to research, we reformulate the typical approach to
research in psychology to harmonize inevitably causal theories with the rest of
the research pipeline. We present a new process which begins with the
incorporation of techniques from the confluence of causal discovery and machine
learning for the development, validation, and transparent formal specification
of theories. We then present methods for reducing the complexity of the fully
specified theoretical model into the fundamental submodel relevant to a given
target hypothesis. From here, we establish whether or not the quantity of
interest is estimable from the data, and if so, propose the use of
semi-parametric machine learning methods for the estimation of causal effects.
The overall goal is the presentation of a new research pipeline which can (a)
facilitate scientific inquiry compatible with the desire to test causal
theories (b) encourage transparent representation of our theories as
unambiguous mathematical objects, (c) to tie our statistical models to specific
attributes of the theory, thus reducing under-specification problems frequently
resulting from the theory-to-model gap, and (d) to yield results and estimates
which are causally meaningful and reproducible. The process is demonstrated
through didactic examples with real-world data, and we conclude with a summary
and discussion of limitations.
