Causal Inference in Psychology and Neuroscience: From Association to Causation

<p>In psychology and neuroscience, inferring causality in non-experimental studies is almost taboo, because data in these studies, e.g., survey data and resting-state neuroimaging data, are often contaminated by unmeasured confounders. Psychologists and neuroscientists are often cautious about their results, and reluctant to make false claims about causality in non-experimental studies. Therefore, they adopt less stringent statistical analysis techniques that can only infer associational relations. However, the ambiguity about causality in traditional statistical analysis creates much confusion in interpreting analytical results - some studies make implicit causal claims about their results using words such as “impacts”, “lead to” and “affects”. This misinterpretation might lead to destructive consequences, e.g., mistakenly identifying a non-existing causal effect from a treatment would potentially harm the patient. To clear this confusion and better articulate the causal relations in non-experimental studies, methods are developed recently to formalize the procedure for inferring causality. Here, we demonstrate the application of causal inference methods in psychology and neuroscience using three empirical studies based on survey data and resting-state neuroimaging data (fMRI and MEG). We also highlight the limitation of these causal inference methods and to what extend causal relations can be recovered from non-experimental data.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2022

Author dc:creator

• Liang, Dehua

Contributors dc:contributor

• Uri Maoz
• Aaron Schurger
• Frederick Eberhardt

• causal inference
• machine learning
• causality
• fMRI
• MEG
• survey data
• Applied Behavior Analysis
• Artificial Intelligence and Robotics
• Cognitive Neuroscience
• Cognitive Psychology
• Computational Neuroscience
• Data Science

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/32

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1032