@inproceedings{9bee0377e7fd4eeaa6f0c50339c4e412,
title = "Causality on longitudinal data: Stable specification search in constrained structural equation modeling",
abstract = "Developing causal models from observational longitudinal studies is an important, ubiquitous problem in many disciplines. A disadvantage of current causal discover algorithms, however, is the inherent instability in structure estimation. With finite data samples small changes in the data can lead to completely different optimal structures. The present work presents a new causal discovery algorithm for longitudinal data that is robust for finite data samples. We validate our approach on a simulated data set and real-world data on Chronic Fatigue Syndrome patients.",
keywords = "Causal modeling, Longitudinal data, Multi-objective evolutionary algorithm, Stability selection, Structural equation model",
author = "Ridho Rahmadi and Perry Groot and Marianne Heins and Hans Knoop and Tom Heskes",
note = "Publisher Copyright: Copyright {\textcopyright} 2015 for This Paper by Its Authors.; 1st International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015 - Workshop co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 ; Conference date: 11-09-2015",
year = "2015",
language = "English",
volume = "1425",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
pages = "101--107",
booktitle = "Causality on longitudinal data",
}