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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Liang, Dehua"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Uri Maoz","Aaron Schurger","Frederick Eberhardt"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12-01T08:00:00Z","date_published":"2022-12-01T08:00:00Z","updated_at":"2026-07-24T01:38:31Z","subjects":["causal inference","machine learning","causality","fMRI","MEG","survey data","Applied Behavior Analysis","Artificial Intelligence and Robotics","Cognitive Neuroscience","Cognitive Psychology","Computational Neuroscience","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/32","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Uri Maoz","Aaron Schurger","Frederick Eberhardt"]},{"key":"dc:creator","label":"Author","values":["Liang, Dehua"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-12-07T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["causal inference","machine learning","causality","fMRI","MEG","survey data","Applied Behavior Analysis","Artificial Intelligence and Robotics","Cognitive Neuroscience","Cognitive Psychology","Computational Neuroscience","Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/32"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:source","label":"Dc Source","values":["D. Liang, \"Causal inference in psychology and neuroscience: From association to causation,\" Ph.D. dissertation, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000420"]},{"key":"dc:title","label":"Title","values":["Causal Inference in Psychology and Neuroscience: From Association to Causation"]}]}],"canonical_facts":{"dc:contributor":["Uri Maoz","Aaron Schurger","Frederick Eberhardt"],"dc:creator":["Liang, Dehua"],"dc:date.available":["2024-12-07T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/32"],"dc:source":["D. Liang, \"Causal inference in psychology and neuroscience: From association to causation,\" Ph.D. dissertation, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000420"],"dc:subject":["causal inference","machine learning","causality","fMRI","MEG","survey data","Applied Behavior Analysis","Artificial Intelligence and Robotics","Cognitive Neuroscience","Cognitive Psychology","Computational Neuroscience","Data Science"],"dc:title":["Causal Inference in Psychology and Neuroscience: From Association to Causation"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:31Z"}