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Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling
[article]
2017
arXiv
pre-print
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A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. ...
We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. ...
Stable specification search for longitudinal data Stable Specification Search for Longitudinal data (S3L) is an extension of S3C. ...
arXiv:1605.06838v3
fatcat:kegaehaetbajnhbxxqcjgulbyq
Causality on longitudinal data: Stable specification search in constrained structural equation modeling
2017
Statistical Methods in Medical Research
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We model causal models using structural equation models. Models are scored along two objectives: the model fit and the model complexity. ...
Developing causal models from observational longitudinal studies is an important, ubiquitous problem in many disciplines. ...
Stable Specification Search Our method, stable specification search [13] , can be divided into two phases (see Figure 2 ). The first phase is search, performing exploratory search over SEM models. ...
doi:10.1177/0962280217713347
pmid:28657454
fatcat:g32a6pjhzbbitevqxbiviagj7y
Causality on cross-sectional data: Stable specification search in constrained structural equation modeling
2017
Applied Soft Computing
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The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection ...
Structure search is performed over Structural Equation Models. Our approach uses exploratory search but allows incorporation of prior background knowledge. ...
Structure search is performed over Structural Equation Models (SEM), which is the most widely used language for causal discovery in various scientific disciplines. ...
doi:10.1016/j.asoc.2016.10.003
fatcat:ymkinn4se5crpphx67i3a7gdxy
The stablespec package for causal discovery on cross-sectional and longitudinal data in R
2018
Neurocomputing
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The method aims at causal discovery on both cross-sectional and longitudinal data through stable specification search in constrained structural equation models. © 2017 Elsevier B.V. ...
Stable specification search is a novel causal discovery method based on [5] , for cross-sectional data (S3C), and [6] , for longitudinal data (S3L). ...
doi:10.1016/j.neucom.2017.10.064
fatcat:3rqsmaaoznalnknglb3uqd7sqm
Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package
2014
Journal of Causal Inference
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An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. ...
Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments ...
In the longitudinal settings, G-computation is an identifiability result derived from the sequential randomization assumption implied by a causal graph or the nonparametric structural equation (NPSEM) ...
doi:10.1515/jci-2013-0025
pmid:26046009
pmcid:PMC4452010
fatcat:lrvfyfnltvfapdjjmeoycmatpm
Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data
2018
Frontiers in Genetics
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We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural ...
In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. ...
Leavitt and the Track, PREDICT Investigators for their contribution to collect TRACK-HD and PREDICT-HD data, and NINDS dbGap data repository (accession number phs000222.v3). ...
doi:10.3389/fgene.2018.00430
pmid:30333854
pmcid:PMC6176748
fatcat:7t7nnchawfbpnggef4jjiiy44y
Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials
2018
Methods of Information in Medicine
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the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. ...
We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and ...
We define a data-generating process (DGP) to be such a time-constrained function (e.g., the univariate structural equations in White and Lu, 2010 [30] ), and call the structural equation expression of ...
doi:10.3414/me16-02-0044
pmid:29621835
pmcid:PMC6087468
fatcat:yboc7hntebbv5hcfrhs4npeof4
Panel data and models of change: A comparison of first difference and conventional two-wave models
1985
Social Science Research
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The greatest potential contribution of longitudinal data is that they permit empirical analyses of dynamic aspects of behavior. ...
Panel data on individuals, families, and their life conditions are becoming increasingly available and open new possibilities for analysis. But they also present new challenges and problems. ...
Given these theoretical and data limitations, major advances from the use of longitudinal data for the estimation of truly dynamic models are apt to be slow in coming. ...
doi:10.1016/0049-089x(85)90013-4
fatcat:5bpkxutgijdltfkqzxy2vxvzgm
Network Data
[article]
2019
arXiv
pre-print
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I emphasize (i) dyadic regression analysis incorporating unobserved agent-specific heterogeneity and supporting causal inference, (ii) techniques for estimating, and conducting inference on, summary network ...
Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another. ...
The second exploits the exponential family structure of the model and conditions on a sufficient statistic for A 0 . Both estimates have antecedents in the literature on panel data. ...
arXiv:1912.06346v1
fatcat:viy67tqtbrhvvoifldgjeqt3nu
The Gaussian Graphical Model in Cross-sectional and Time-series Data
[article]
2018
arXiv
pre-print
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The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. ...
When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means---the between-subjects network. ...
Acknowledgements We would like to thank Laura Bringmann, Noémi Schuurman, Oisín Ryan and Ellen Hamaker for helpful tips and invigorating discussions, and Katharina Jorgensen for valuable comments on earlier ...
arXiv:1609.04156v6
fatcat:sdbfaxapx5ed3ox6swzk4rdra4
At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study
2018
Multivariate Behavioral Research
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Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. ...
KEYWORDS Dynamic structural equation modeling (DSEM); intensive longitudinal data; dynamic multilevel modeling; multilevel time series analysis ABSTRACT With the growing popularity of intensive longitudinal ...
Such structural missing data are quite common in intensive longitudinal data. ...
doi:10.1080/00273171.2018.1446819
pmid:29624092
fatcat:s3zw6mudqjcflngub3mildabuu
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
2018
Multivariate Behavioral Research
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The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. ...
When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. ...
An earlier version of this paper has been adapted as a chapter in the dissertation of the main author (Epskamp, 2017b) . ...
doi:10.1080/00273171.2018.1454823
pmid:29658809
fatcat:23jaxodovzd2hnaxsrchchkoeu
Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box
2021
Communications Biology
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Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo ...
brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. ...
This project was also undertaken thanks in part to the following funding awards to Y. ...
doi:10.1038/s42003-021-02133-x
pmid:34021244
fatcat:wlncznrxbzatxmsapuy22446fa
A Nonlinear Dynamic Model Applied to Data with Two Times of Measurement
2016
Journal for Person-Oriented Research
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A good model of developmental phenomena should not only "explain" the data in the sense that the model is not falsified by the data but it should also be built on the basic theoretical assumptions about ...
It is pointed out in the article that it might be possible to use nonlinear dynamic system modeling also in contexts where data are available only from a few measurement occasions. ...
For instance, standard methods for measuring fit within the structural equation modeling tradition have limitations in that many fit measures are based on the similarity between the predicted and actual ...
doi:10.17505/jpor.2016.06
fatcat:huiffyxezbdlbkwbbyehedfnxy
Longitudinal Data Analysis with Structural Equations
2008
Methodology: European Journal of Research Methods for the Behavioral and Social Sciences
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In this paper we review different structural equation models for the analysis of longitudinal data: (a) univariate models of observable variables, (b) multivariate models of observable variables, (c) models ...
single level and multilevel measurement, and (i) other advances in SEM of longitudinal data (latent growth curve model, latent difference score, etc.). ...
Longitudinal data analysis with structural equations Since Jöreskog (1969) , Keesling (1972) and Wiley (1973) first developed the statistical model of structural equations, it has become one of the ...
doi:10.1027/1614-2241.4.1.37
fatcat:qvduqyciszfolbacxjguu2k37m
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