Affiliation:
1. Halicioglu Data Science Institute, Department of Philosophy University of California San Diego La Jolla California USA
2. Department of Psychology Yale University New Haven Connecticut USA
Abstract
AbstractCausal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that “correlation does not mean causation”: there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task.This article is categorized under:
Psychology > Theory and Methods
Philosophy > Foundations of Cognitive Science
Subject
General Psychology,General Medicine,General Neuroscience
Cited by
4 articles.
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