Wprowadzenie do teorii wnioskowania przyczynowego dla psychologów: testowalne i nietestowalne założenia przyczynowe i statystyczne
Introduction to Causal Inference for Psychologists: Testable and Non-Testable Causal and Statistical Assumptions

Summary/Abstract: The main goal of basic research is to answer causal questions. Generally, only the statisti- cal part of this process tends to proceed in a partially formal way and according to clearly defined rules. At the same time, causal relations are often treated informally or implicitly in a way that is prone to difficult-to-detect errors. This introduction aims to show psy- chology researchers some of the great benefits of approaching causal issues using a for- mal 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 ad- justments. 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.

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