Applying Causal Discovery to Intensive Longitudinal Data

Britt Stevenson, Erich Kummerfeld, Jennifer E. Merrill

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Intensive longitudinal data (ILD) could be a solution for two problems in psychology: 1) In traditional experiments and survey studies, findings are not necessarily representative of the real-life constructs and relationships studied, and 2) Group-level analyses commonly mischaracterize or obscure relationships for individuals. Popular analytic methods within psychology are currently not well-equipped to use ILD for causal discovery and causal inference, however. We have performed the first causal discovery analysis on ILD, encountered some challenges, and developed some solutions to these challenges. This paper describes our application of causal discovery to an example ILD dataset, and addresses two particular challenges that arose: 1) How should one address variables measured on different timelines, and 2) What number of observations is needed for individual-level analysis.

Original languageEnglish (US)
Pages (from-to)20-29
Number of pages10
JournalProceedings of Machine Learning Research
StatePublished - 2021
Event2021 Causal Analysis Workshop Series, CAWS 2021 - Minneapolis, United States
Duration: Jul 16 2021 → …

Bibliographical note

Funding Information:
BLS was supported by funding from T32DA037183. EK was supported by funding from Grant No. NCRR 1UL1TR002494-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2021 B.L.S. PhD, E.K. PhD & J.E.M. PhD.


  • alcohol use
  • causal discovery
  • ecological momentary assessment
  • intensive longitudinal data
  • mood
  • precision medicine


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