Introduction to Causal Inference for Psychologists: Testable and Non-Testable Causal and Statistical Assumptions

Borysław Paulewicz

Jagiellonian University, Institute of Psychology
https://orcid.org/0000-0002-1270-2988


Abstract

The main goal of basic research is to answer causal questions. Generally, only the statistical part of this process tends to proceed in a partially formal way and according to clearly defined rules. At the same time, the causal relations are often treated informally or implicitly in a way that is prone to difficult-to-detect errors. This introduction aims to show psychology researchers some of the great benefits of approaching causal issues using a formal theory of causal inference. In this part, I discuss the non-obvious status and role of causal and statistical assumptions in causal inference. After covering, in a simple setting, the general shape of inference from causal assumptions, statistical assumptions, and data to causal effects, I outline, from a contemporary perspective, the limits of applicability of the general linear model. Then, I introduce the formal part of Pearl’s theory that relies on graphs. Using these tools, I show how one can analyze and interpret the results of an experiment on short-term memory search, and I discuss the back-door and front-door adjustments. To present the mathematical part of the theory in an accessible way without overly simplifying it, I illustrate some issues by using simulations written in R.


Keywords:

causality, causal inference, causal calculus, research methods, metatheory, statistical inference, Bayesian inference


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Published
2023-10-26

Cited by

Paulewicz, B. (2023). Introduction to Causal Inference for Psychologists: Testable and Non-Testable Causal and Statistical Assumptions. The Review of Psychology, 66(1), 209–240. https://doi.org/10.31648/przegldpsychologiczny.9731

Borysław Paulewicz 
Jagiellonian University, Institute of Psychology
https://orcid.org/0000-0002-1270-2988