A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists

TL;DR

This paper introduces a new causal research pipeline for psychologists and social scientists that integrates causal discovery, machine learning, and transparent modeling to improve causal inference and theory testing.

Contribution

It proposes a comprehensive, step-by-step process for developing, specifying, and estimating causal theories, bridging the gap between causal reasoning and empirical research in social sciences.

Findings

The pipeline facilitates transparent and reproducible causal analysis.

It demonstrates the approach with real-world data examples.

The method encourages formal, unambiguous representation of theories.

Abstract

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.