Date of Award

Fall 12-2022

Document Type


Degree Name

Doctor of Philosophy (PhD)


Computational and Data Sciences

First Advisor

Uri Maoz

Second Advisor

Aaron Schurger

Third Advisor

Frederick Eberhardt


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.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Saturday, December 07, 2024