2021-12-01-stevenson21a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	stevenson21a	0	Applying Causal Discovery to Intensive Longitudinal Data	20	29	20-29	20	false	Stevenson, Brittany and Kummerfeld, Erich and Merrill, Jennifer	given family Brittany Stevenson given family Erich Kummerfeld given family Jennifer Merrill	2021-12-01		Proceedings of The 2021 Causal Analysis Workshop Series	160	inproceedings	date-parts 2021 12 1	https://proceedings.mlr.press/v160/stevenson21a/stevenson21a.pdf