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Applying Causal Discovery to Intensive Longitudinal Data

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.