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Causality is a fundamental part of the scientific endeavour to understand theworld. Unfortunately, causality is still taboo in much of psychology and socialscience. Motivated by a growing number of recommendations for the importance ofadopting causal approaches to research, we reformulate the typical approach toresearch in psychology to harmonize inevitably causal theories with the rest ofthe research pipeline. We present a new process which begins with theincorporation of techniques from the confluence of causal discovery and machinelearning for the development, validation, and transparent formal specificationof theories. We then present methods for reducing the complexity of the fullyspecified theoretical model into the fundamental submodel relevant to a giventarget hypothesis. From here, we establish whether or not the quantity ofinterest is estimable from the data, and if so, propose the use ofsemi-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 causaltheories (b) encourage transparent representation of our theories asunambiguous mathematical objects, (c) to tie our statistical models to specificattributes of the theory, thus reducing under-specification problems frequentlyresulting from the theory-to-model gap, and (d) to yield results and estimateswhich are causally meaningful and reproducible. The process is demonstratedthrough didactic examples with real-world data, and we conclude with a summaryand discussion of limitations.

A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists