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@article{baba_partial_2004,
author = {Baba, Kunihiro and Shibata, Ritei and Sibuya, Masaaki},
doi = {10.1111/j.1467-842X.2004.00360.x},
issn = {1467-842X},
journal = {Australian \& New Zealand Journal of Statistics},
keywords = {elliptical distribution, exchangeability, graphical modelling, monotone transformation},
language = {en},
number = {4},
pages = {657--664},
title = {Partial Correlation and Conditional Correlation as Measures of Conditional Independence},
urldate = {2023-04-11},
volume = {46},
year = {2004},
bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-842X.2004.00360.x},
bdsk-url-2 = {https://doi.org/10.1111/j.1467-842X.2004.00360.x}}
@article{beller_differential_2013,
author = {Beller, Johannes and Baier, Dirk},
copyright = {Copyright (c)},
doi = {10.5964/ejop.v9i2.528},
issn = {1841-0413},
journal = {Europe's Journal of Psychology},
keywords = {differential effects, generalized additive models, heterogeneous, linear regression, non-linear, quantile regression, violent crime},
language = {en},
number = {2},
pages = {378--384},
shorttitle = {Differential {Effects}},
title = {Differential Effects: Are the Effects Studied by Psychologists Really Linear and Homogeneous?},
volume = {9},
year = {2013},
bdsk-url-1 = {https://ejop.psychopen.eu/index.php/ejop/article/view/528},
bdsk-url-2 = {https://doi.org/10.5964/ejop.v9i2.528}}
@book{bickel_mathematical_2015,
abstract = {Volume I presents fundamental, classical statistical concepts at the doctorate level without using measure theory. It gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. Volume II covers a number of topics that are important in current measure theory and practice. It emphasizes nonparametric methods which can really only be implemented with modern computing power on large and complex data sets. In addition, the set includes a large number of problems with more difficult ones appearing with hints and partial solutions for the instructor.},
address = {New York},
author = {Bickel, Peter J. and Doksum, Kjell A.},
doi = {10.1201/9781315369266},
isbn = {978-1-315-36926-6},
month = dec,
publisher = {Chapman and Hall/CRC},
shorttitle = {Mathematical {Statistics}},
title = {Mathematical Statistics: {Basic} Ideas and Selected Topics, {Volumes} {I}-{II}},
year = {2015},
bdsk-url-1 = {https://doi.org/10.1201/9781315369266}}
@book{bollen1993testing,
title={Testing structural equation models},
author={Bollen, Kenneth A and Long, J Scott},
volume={154},
year={1993},
publisher={Sage}
}
@book{bollen_structural_1989,
abstract = {Currently not available for this book.},
author = {Bollen, Kenneth A.},
file = {ProQuest Ebook Snapshot:/Users/Kyuri1/Zotero/storage/V75IW5M7/detail.html:text/html},
isbn = {978-1-118-61916-2},
publisher = {John Wiley \& Sons},
title = {Structural Equations with Latent Variables},
urldate = {2023-03-31},
year = {1989},
bdsk-url-1 = {http://ebookcentral.proquest.com/lib/uunl/detail.action?docID=7103880}}
@incollection{bollen_eight_2013,
abstract = {Causality was at the center of the early history of structural equation models (SEMs) which continue to serve as the most popular approach to causal analysis in the social sciences. Through decades of development, critics and defenses of the capability of SEMs to support causal inference have accumulated. A variety of misunderstandings and myths about the nature of SEMs and their role in causal analysis have emerged, and their repetition has led some to believe they are true. Our chapter is organized by presenting eight myths about causality and SEMs in the hope that this will lead to a more accurate understanding. More specifically, the eight myths are the following: (1) SEMs aim to establish causal relations from associations alone, (2) SEMs and regression are essentially equivalent, (3) no causation without manipulation, (4) SEMs are not equipped to handle nonlinear causal relationships, (5) a potential outcome framework is more principled than SEMs, (6) SEMs are not applicable to experiments with randomized treatments, (7) mediation analysis in SEMs is inherently noncausal, and (8) SEMs do not test any major part of the theory against the data. We present the facts that dispel these myths, describe what SEMs can and cannot do, and briefly present our critique of current practice using SEMs. We conclude that the current capabilities of SEMs to formalize and implement causal inference tasks are indispensible; its potential to do more is even greater.},
address = {Dordrecht},
author = {Bollen, Kenneth A. and Pearl, Judea},
booktitle = {Handbook of {Causal} {Analysis} for {Social} {Research}},
doi = {10.1007/978-94-007-6094-3_15},
editor = {Morgan, Stephen L.},
file = {Submitted Version:/Users/Kyuri1/Zotero/storage/DA769JJX/Bollen and Pearl - 2013 - Eight Myths About Causality and Structural Equatio.pdf:application/pdf},
isbn = {978-94-007-6094-3},
keywords = {Causal Effect, Causal Relation, Latent Variable, Path Analysis, Structural Equation Model},
language = {en},
pages = {301--328},
publisher = {Springer Netherlands},
series = {Handbooks of {Sociology} and {Social} {Research}},
title = {Eight {Myths} {About} {Causality} and {Structural} {Equation} {Models}},
year = {2013}}
@article{borsboom_network_2021,
author = {Borsboom, Denny and Deserno, Marie K. and Rhemtulla, Mijke and Epskamp, Sacha and Fried, Eiko I. and McNally, Richard J. and Robinaugh, Donald J. and Perugini, Marco and Dalege, Jonas and Costantini, Giulio and Isvoranu, Adela-Maria and Wysocki, Anna C. and van Borkulo, Claudia D. and van Bork, Riet and Waldorp, Lourens J.},
copyright = {2021 Springer Nature Limited},
doi = {10.1038/s43586-021-00055-w},
issn = {2662-8449},
journal = {Nature Reviews Methods Primers},
keywords = {Scientific data, Statistics},
language = {en},
number = {1},
pages = {1--18},
title = {Network analysis of multivariate data in psychological science},
urldate = {2022-12-09},
volume = {1},
year = {2021},
bdsk-url-1 = {https://www.nature.com/articles/s43586-021-00055-w},
bdsk-url-2 = {https://doi.org/10.1038/s43586-021-00055-w}}
@article{borsboom_network_2017,
author = {Borsboom, Denny},
doi = {10.1002/wps.20375},
issn = {2051-5545},
journal = {World Psychiatry},
keywords = {diagnosis, mental disorders, mental health, network approach, Psychopathology, resilience, symptom networks, treatment, vulnerability},
language = {en},
number = {1},
pages = {5--13},
title = {A network theory of mental disorders},
volume = {16},
year = {2017},
bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wps.20375},
bdsk-url-2 = {https://doi.org/10.1002/wps.20375}}
@article{briganti_tutorial_2022,
abstract = {Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data. (PsycInfo Database Record (c) 2022 APA, all rights reserved)},
author = {Briganti, Giovanni and Scutari, Marco and McNally, Richard J.},
doi = {10.1037/met0000479},
issn = {1939-1463},
journal = {Psychological Methods},
keywords = {Algorithms, Family Relations, Inference, Major Depression, Models, Psychopathology, Simulation, Statistical Probability},
title = {A tutorial on bayesian networks for psychopathology researchers},
year = {2022}}
@article{Bongers2021,
author = {Stephan Bongers and Patrick Forr{\'e} and Jonas Peters and Joris M. Mooij},
title = {{Foundations of structural causal models with cycles and latent variables}},
volume = {49},
journal = {The Annals of Statistics},
number = {5},
publisher = {Institute of Mathematical Statistics},
pages = {2885--2915},
keywords = {causal graph, counterfactuals, cycles, interventions, marginalization, Markov properties, solvability, Structural causal models},
year = {2021},
doi = {10.1214/21-AOS2064}}
@article{bongers2018theoretical,
title={Theoretical aspects of cyclic structural causal models},
author={Bongers, Stephan and Peters, Jonas and Sch{\"o}lkopf, Bernhard and Mooij, Joris M},
journal={arXiv preprint arXiv:1611.06221},
year={2018}
}
@article{BorsboomCramer2013,
author = {Borsboom, Denny and Cramer, Ang\'{e}lique O.J.},
title = {Network Analysis: An Integrative Approach to the Structure of Psychopathology},
journal = {Annual Review of Clinical Psychology},
volume = {9},
number = {1},
pages = {91--121},
year = {2013},
doi = {10.1146/annurev-clinpsy-050212-185608},
eprint = {https://doi.org/10.1146/annurev-clinpsy-050212-185608}
}
@article{canonne_testing_2018,
author = {Canonne, Cl{\'e}ment L. and Diakonikolas, Ilias and Kane, Daniel M. and Stewart, Alistair},
doi = {10.48550/arXiv.1711.11560},
keywords = {Computer Science - Computational Complexity, Computer Science - Data Structures and Algorithms, Computer Science - Discrete Mathematics, Mathematics - Probability, Mathematics - Statistics Theory},
journal = {arXiv preprint arXiv:1711.11560},
title = {Testing Conditional Independence of Discrete Distributions},
year = {2018},
bdsk-url-1 = {http://arxiv.org/abs/1711.11560},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.1711.11560}}
@misc{chickering2013learning,
title={Learning Equivalence Classes of Bayesian Networks Structures},
author={David Maxwell Chickering},
year={2013},
eprint={1302.3566},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
@article{coccurello_anhedonia_2019,
abstract = {Anhedonia is an elusive symptom in depression symptomatology. The present review frames the notion of anhedonia as reduced ability to experience pleasure and diminished sensitivity to rewarding stimuli such as palatable food or social interaction within the context of appetite dysregulation in depression, addressing the main neural networks involved in the alteration of brain reward processing. This circuit-based framework focuses on selected brain regions such as lateral hypothalamus, ventral pallidum, lateral habenula and mesocorticolimbic target areas such as nucleus accumbens and ventral tegmental area. The examination in particular of the role of dopamine, orexin and GABAergic neurotransmission is complemented by the exploration of the endocannabinoid signaling as homeostatic, anti-stress system and its relevance in depression pathophysiology and anhedonia symptoms.},
author = {Coccurello, Roberto},
doi = {10.1016/j.bbr.2019.112041},
file = {ScienceDirect Full Text PDF:/Users/Kyuri1/Zotero/storage/NSWYBPAL/Coccurello - 2019 - Anhedonia in depression symptomatology Appetite d.pdf:application/pdf;ScienceDirect Snapshot:/Users/Kyuri1/Zotero/storage/C26XSFQG/S0166432819303225.html:text/html},
issn = {0166-4328},
journal = {Behavioural Brain Research},
keywords = {Anhedonia, Depressive disorder, Endocannabinoid system, Lateral habenula, Lateral hypothalamus, Reward processing},
language = {en},
month = oct,
pages = {112041},
shorttitle = {Anhedonia in depression symptomatology},
title = {Anhedonia in depression symptomatology: {Appetite} dysregulation and defective brain reward processing},
url = {https://www.sciencedirect.com/science/article/pii/S0166432819303225},
urldate = {2023-04-28},
volume = {372},
year = {2019},
bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0166432819303225},
bdsk-url-2 = {https://doi.org/10.1016/j.bbr.2019.112041}}
@article{constantin2022,
author = {Mihai Constantin and Ang{\'{e}}lique O. J. Cramer},
doi = {10.17605/OSF.IO/ZKAXU},
journal = {OSF},
title = {Sample Size Recommendations for Estimating Cross-Sectional Network Models},
year = {2022},
bdsk-url-1 = {https://doi.org/10.17605/OSF.IO/ZKAXU}}
@article{dablander2019node,
author = {Dablander, Fabian and Hinne, Max},
copyright = {2019 The Author(s)},
doi = {10.1038/s41598-019-43033-9},
issn = {2045-2322},
journal = {Scientific Reports},
keywords = {Human behaviour, Network models},
language = {en},
number = {1},
pages = {6846},
title = {Node centrality measures are a poor substitute for causal inference},
urldate = {2022-12-10},
volume = {9},
year = {2019}}
@article{de_ridder_psychological_2008,
author = {de Ridder, Denise and Geenen, Rinie and Kuijer, Roeline and van Middendorp, Henri{\"e}t},
doi = {10.1016/S0140-6736(08)61078-8},
issn = {0140-6736},
journal = {The Lancet},
language = {en},
month = jul,
number = {9634},
pages = {246--255},
title = {Psychological adjustment to chronic disease},
url = {https://www.sciencedirect.com/science/article/pii/S0140673608610788},
urldate = {2023-04-26},
volume = {372},
year = {2008},
bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0140673608610788},
bdsk-url-2 = {https://doi.org/10.1016/S0140-6736(08)61078-8}}
@article{diego2012,
author = {Diego Colombo and Marloes H. Maathuis and Markus Kalisch and Thomas S. Richardson},
doi = {10.1214/11-AOS940},
journal = {The Annals of Statistics},
keywords = {Causal structure learning, consistency, FCI algorithm, high-dimensionality, maximal ancestral graphs (MAGs), partial ancestral graphs (PAGs), RFCI algorithm, Sparsity},
number = {1},
pages = {294--321},
publisher = {Institute of Mathematical Statistics},
title = {{Learning high-dimensional directed acyclic graphs with latent and selection variables}},
volume = {40},
year = {2012},
bdsk-url-1 = {https://doi.org/10.1214/11-AOS940}}
@article{de2009comparison,
title={A comparison of structural distance measures for causal Bayesian network models},
author={de Jongh, Martijn and Druzdzel, Marek J},
journal={Recent Advances in Intelligent Information Systems},
pages={443--456},
year={2009},
publisher={Academic Publishing House EXIT Warsaw, Poland}
}
@article{drton_structure_2017,
author = {Drton, Mathias and Maathuis, Marloes H.},
doi = {10.1146/annurev-statistics-060116-053803},
journal = {Annual Review of Statistics and Its Application},
keywords = {Bayesian network, graphical model, Markov random field, model selection, multivariate statistics, network reconstruction},
number = {1},
pages = {365--393},
title = {Structure Learning in Graphical Modeling},
urldate = {2022-10-23},
volume = {4},
year = {2017},
bdsk-url-1 = {https://doi.org/10.1146/annurev-statistics-060116-053803}}
@inproceedings{eberhardt2010,
author = {Eberhardt, Frederick and Hoyer, Patrik and Scheines, Richard},
booktitle = {Proceedings of the {Thirteenth} {International} {Conference} on {Artificial} {Intelligence} and {Statistics}},
language = {en},
pages = {185--192},
organization={JMLR Workshop and Conference Proceedings},
title = {Combining Experiments to Discover Linear Cyclic Models with Latent Variables},
year = {2010},
bdsk-url-1 = {https://proceedings.mlr.press/v9/eberhardt10a.html}}
@article{eigenmann_structure_2017,
annote = {Comment: 18 pages, 17 figures, UAI 2017},
author = {Eigenmann, Marco F. and Nandy, Preetam and Maathuis, Marloes H.},
doi = {10.48550/arXiv.1707.07560},
keywords = {Statistics - Methodology},
journal = {arXiv preprint arXiv:1707.07560},
title = {Structure Learning of Linear {Gaussian} Structural Equation Models with Weak Edges},
urldate = {2023-03-16},
year = {2017},
bdsk-url-1 = {http://arxiv.org/abs/1707.07560},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.1707.07560}}
@article{epskamp_tutorial_2018,
annote = {Comment: In press in Psychological Methods (DOI: 10.1037/met0000167)},
author = {Epskamp, Sacha and Fried, Eiko I.},
doi = {10.1037/met0000167},
issn = {1939-1463, 1082-989X},
journal = {Psychological Methods},
keywords = {Statistics - Methodology, Statistics - Applications},
number = {4},
pages = {617--634},
title = {A Tutorial on Regularized Partial Correlation Networks},
volume = {23},
year = {2018},
bdsk-url-1 = {http://arxiv.org/abs/1607.01367},
bdsk-url-2 = {https://doi.org/10.1037/met0000167}}
@article{epskamp_estimating_2018,
author = {Epskamp, Sacha and Borsboom, Denny and Fried, Eiko I.},
doi = {10.3758/s13428-017-0862-1},
file = {Full Text:/Users/Kyuri1/Zotero/storage/JYET4RTP/Epskamp et al. - 2018 - Estimating psychological networks and their accura.pdf:application/pdf},
issn = {1554-3528},
journal = {Behavior Research Methods},
keywords = {Bootstrap, Dimensional Measurement Accuracy, Female, Humans, Network psychometrics, Neural Networks, Computer, Psychological networks, Psychophysiology, Replicability, Stress Disorders, Post-Traumatic, Tutorial},
language = {eng},
month = feb,
number = {1},
pages = {195--212},
pmcid = {PMC5809547},
pmid = {28342071},
shorttitle = {Estimating psychological networks and their accuracy},
title = {Estimating psychological networks and their accuracy: {A} tutorial paper},
volume = {50},
year = {2018},
bdsk-url-1 = {https://doi.org/10.3758/s13428-017-0862-1}}
@article{epskamp_gaussian_2018,
author = {Epskamp, Sacha and Waldorp, Lourens J. and M{\~o}ttus, Ren{\'e} and Borsboom, Denny},
doi = {10.1080/00273171.2018.1454823},
issn = {0027-3171},
journal = {Multivariate Behavioral Research},
keywords = {exploratory-data analysis, multilevel modeling, multivariate analysis, network modeling, Time-series analysis},
number = {4},
pages = {453--480},
pmid = {29658809},
title = {The {Gaussian} Graphical Model in Cross-Sectional and Time-Series Data},
volume = {53},
year = {2018},
bdsk-url-1 = {https://doi.org/10.1080/00273171.2018.1454823}}
@article{friedman_sparse_2008,
author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
doi = {10.1093/biostatistics/kxm045},
issn = {1468-4357},
journal = {Biostatistics},
keywords = {Algorithms, Animals, Biometry, Data Interpretation, Statistical, Humans, Models, Statistical, Neural Networks, Computer, Proteomics, Reference Values, Regression Analysis, Sample Size, Signal Transduction, Time Factors},
language = {eng},
number = {3},
pages = {432--441},
pmcid = {PMC3019769},
pmid = {18079126},
title = {Sparse inverse covariance estimation with the graphical lasso},
volume = {9},
year = {2008},
bdsk-url-1 = {https://doi.org/10.1093/biostatistics/kxm045}}
@article{forre_markov_2017,
annote = {Comment: 131 pages},
author = {Forr{\'e}, Patrick and Mooij, Joris M.},
doi = {10.48550/arXiv.1710.08775},
file = {arXiv Fulltext PDF:/Users/Kyuri1/Zotero/storage/TTVVFS25/Forr{\'e} and Mooij - 2017 - Markov Properties for Graphical Models with Cycles.pdf:application/pdf;arXiv.org Snapshot:/Users/Kyuri1/Zotero/storage/79AHRXV6/1710.html:text/html},
journal={arXiv preprint arXiv:1710.08775},
title = {Markov Properties for Graphical Models with Cycles and Latent Variables},
year = {2017},
bdsk-url-1 = {http://arxiv.org/abs/1710.08775},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.1710.08775}}
@incollection{geiger_logic_1990,
title={On the logic of causal models},
author={Geiger, Dan and Pearl, Judea},
booktitle={Machine intelligence and pattern recognition},
volume={9},
editor = {Shachter, Ross D. and Levitt, Tod S. and Kanal, Laveen N. and Lemmer, John F.},
pages={3--14},
year={1990},
publisher = {North-Holland},
doi = {10.1016/B978-0-444-88650-7.50006-8},
shorttitle = {On the {Logic} of {Causal} {Models}}
}
@incollection{geiger_d-separation_1990,
author = {Geiger, Dan and Verma, Thomas and Pearl, Judea},
booktitle = {Machine intelligence and pattern recognition},
doi = {10.1016/B978-0-444-88738-2.50018-X},
language = {en},
pages = {139--148},
editor = {Henrion, Max and Shachter, Ross D. and Kanal, Laveen N. and Lemmer, John F.},
publisher = {North-Holland},
series = {Uncertainty in {Artificial} {Intelligence}},
shorttitle = {d-{Separation}},
title = {d-{Separation}: {From} Theorems to Algorithms},
volume = {10},
year = {1990},
bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/B978044488738250018X},
bdsk-url-2 = {https://doi.org/10.1016/B978-0-444-88738-2.50018-X}}
@article{Glymour2019,
doi = {10.3389/fgene.2019.00524},
year = {2019},
publisher = {Frontiers Media {SA}},
volume={10},
pages={524},
author = {Clark Glymour and Kun Zhang and Peter Spirtes},
title = {Review of Causal Discovery Methods Based on Graphical Models},
journal = {Frontiers in Genetics}
}
@article{goertz_fatigue_2021,
author = {Go{\"e}rtz, Yvonne M. J. and Braamse, Annemarie M. J. and Spruit, Martijn A. and Ebadi, Zjala and Van Herck, Maarten and Burtin, Chris and Peters, Jeannette B. and Lamers, Femke and Geerlings, Suzanne E. and Vaes, Anouk W. and van Beers, Martijn and Knoop, Hans},
copyright = {2021 The Author(s)},
doi = {10.1038/s41598-021-00337-z},
file = {Full Text PDF:/Users/Kyuri1/Zotero/storage/RPEIZBFK/Go{\"e}rtz et al. - 2021 - Fatigue in patients with chronic disease results .pdf:application/pdf},
issn = {2045-2322},
journal = {Scientific Reports},
keywords = {Comorbidities, Fatigue},
language = {en},
month = oct,
number = {1},
pages = {20977},
shorttitle = {Fatigue in patients with chronic disease},
title = {Fatigue in patients with chronic disease: results from the population-based {Lifelines} {Cohort} {Study}},
url = {https://www.nature.com/articles/s41598-021-00337-z},
urldate = {2023-04-24},
volume = {11},
year = {2021},
bdsk-url-1 = {https://www.nature.com/articles/s41598-021-00337-z},
bdsk-url-2 = {https://doi.org/10.1038/s41598-021-00337-z}}
@article{haslbeck_sum_2022,
author = {Haslbeck, Jonas M. B. and Ryan, Ois{\'\i}n and Dablander, Fabian},
doi = {10.1037/met0000418},
issn = {1939-1463(Electronic),1082-989X(Print)},
journal = {Psychological Methods},
keywords = {*Diagnosis, *Experimenter Bias, *Mind, *Models, *Statistical Analysis, Causal Analysis, Symptoms},
pages = {1061--1068},
title = {The sum of all fears: {Comparing} networks based on symptom sum-scores.},
volume = {27},
year = {2022},
bdsk-url-1 = {https://doi.org/10.1037/met0000418}}
@article{haslbeck_modeling_2021,
author = {Haslbeck, Jonas M. B. and Ryan, Ois{\'\i}n and Robinaugh, Donald J. and Waldorp, Lourens J. and Borsboom, Denny},
doi = {10.1037/met0000303},
issn = {1939-1463},
journal = {Psychological Methods},
language = {eng},
pmid = {34735175},
shorttitle = {Modeling psychopathology},
title = {Modeling psychopathology: {From} data models to formal theories},
year = {2021},
bdsk-url-1 = {https://doi.org/10.1037/met0000303}}
@article{hallquist2019,
author = {Michael N. Hallquist and Aidan G. C. Wright and Peter C. M. Molenaar},
doi = {10.1080/00273171.2019.1640103},
eprint = {https://doi.org/10.1080/00273171.2019.1640103},
journal = {Multivariate Behavioral Research},
number = {2},
pages = {199--223},
publisher = {Routledge},
title = {Problems with Centrality Measures in Psychopathology Symptom Networks: {Why} Network Psychometrics Cannot Escape Psychometric Theory},
volume = {56},
year = {2021},
bdsk-url-1 = {https://doi.org/10.1080/00273171.2019.1640103}}
@article{huang_sun_white_2016,
title={A FLEXIBLE NONPARAMETRIC TEST FOR CONDITIONAL INDEPENDENCE}, volume={32}, DOI={10.1017/S0266466615000286},
number={6}, journal={Econometric Theory},
publisher={Cambridge University Press},
author={Huang, Meng and Sun, Yixiao and White, Halbert},
year={2016},
pages={1434–1482}}
@article{huang_testing_2010,
author = {Huang, Tzee Ming},
doi = {10.1214/09-AOS770},
issn = {0090-5364},
journal = {The Annals of Statistics},
keywords = {Mathematics - Statistics Theory},
number = {4},
title = {Testing conditional independence using maximal nonlinear conditional correlation},
urldate = {2023-04-15},
volume = {38},
year = {2010},
bdsk-url-1 = {http://arxiv.org/abs/1010.3843},
bdsk-url-2 = {https://doi.org/10.1214/09-AOS770}}
@article{hyttinen_discovering_2013,
annote = {Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)},
author = {Hyttinen, Antti and Hoyer, Patrik O. and Eberhardt, Frederick and Jarvisalo, Matti},
doi = {10.48550/arXiv.1309.6836},
keywords = {Computer Science - Artificial Intelligence},
journal = {arXiv preprint arXiv:1309.6836},
shorttitle = {Discovering {Cyclic} {Causal} {Models} with {Latent} {Variables}},
title = {Discovering Cyclic Causal Models with Latent Variables: {A} General {SAT}-Based Procedure},
urldate = {2023-02-28},
year = {2013},
bdsk-url-1 = {http://arxiv.org/abs/1309.6836},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.1309.6836}}
@article{jamison_influence_1988,
abstract = {This study examined concentration and memory problems in chronic pain patients as they related to emotional distress and interference with daily activity. Three hundred and sixty-three chronic pain patients were divided into two groups based on how much they expressed difficulty in concentrating and remembering things. Each patient was given a physical examination and completed a pain evaluation questionnaire and an SCL-90. Physician ratings of the patients' depression and anxiety were also obtained. The results showed that problems in concentration and memory were related to emotional distress, poor family support, and interference with daily activities. It is suggested that techniques to improve concentration and memory should be incorporated as part of a multidisciplinary pain program.},
author = {Jamison, R. N. and Sbrocco, T. and Parris, W. C.},
doi = {10.2190/ftr1-f9vx-cb8t-wpmc},
issn = {0091-2174},
journal = {International Journal of Psychiatry in Medicine},
keywords = {Activities of Daily Living, Adult, Affective Symptoms, Attention, Chronic Disease, Female, Humans, Male, Memory Disorders, Middle Aged, Pain},
language = {eng},
number = {2},
pages = {183--191},
pmid = {3170081},
title = {The influence of problems with concentration and memory on emotional distress and daily activities in chronic pain patients},
volume = {18},
year = {1988},
bdsk-url-1 = {https://doi.org/10.2190/ftr1-f9vx-cb8t-wpmc}}
@article{kalisch_estimating_2005,
author = {Kalisch, Markus and Buehlmann, Peter},
doi = {10.48550/arXiv.math/0510436},
keywords = {Mathematics - Statistics Theory, 62H20, 62H12 (Primary), 68Q32 (Secondary)},
journal = {arXiv preprint arXiv:math/0510436},
title = {Estimating high-dimensional directed acyclic graphs with the {PC}-algorithm},
year = {2005},
bdsk-url-1 = {http://arxiv.org/abs/math/0510436},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.math/0510436}}
@article{Kossakowski2021,
doi = {10.1037/met0000390},
year = {2021},
publisher = {American Psychological Association ({APA})},
volume = {26},
number = {6},
pages = {719--742},
author = {Jolanda Kossakowski and Lourens J. Waldorp and Han L. J. van der Maas},
title = {The search for causality: A comparison of different techniques for causal inference graphs.},
journal = {Psychological Methods}
}
@article{lauritzen2000graphical,
title={Causal inference from graphical models},
author={Lauritzen, Steffen},
journal={Monographs on Statistics and Applied Probability},
volume={87},
pages={63--108},
year={2001},
publisher={Chapman \& Hall}
}
@book{lauritzen1996graphical,
title={Graphical models},
author={Lauritzen, Steffen},
volume={17},
year={1996},
publisher={Clarendon Press}
}
@article{lawrance_conditional_1976,
author = {Lawrance, A. J.},
doi = {10.2307/2683864},
issn = {0003-1305},
journal = {The American Statistician},
number = {3},
pages = {146--149},
title = {On Conditional and Partial Correlation},
urldate = {2023-03-27},
volume = {30},
year = {1976},
bdsk-url-1 = {https://www.jstor.org/stable/2683864},
bdsk-url-2 = {https://doi.org/10.2307/2683864}}
@article{li_fan2020,
author = {Li, Chun and Fan, Xiaodan},
doi = {10.1002/wics.1489},
eprint = {https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1489},
journal = {WIREs Computational Statistics},
keywords = {conditional independence, hypothesis testing, literature review},
number = {3},
pages = {e1489},
title = {On nonparametric conditional independence tests for continuous variables},
volume = {12},
year = {2020},
bdsk-url-1 = {https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wics.1489},
bdsk-url-2 = {https://doi.org/10.1002/wics.1489}}
@article{liu_nonparanormal_2009,
abstract = {Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula--or "nonparanormal"--for high dimensional inference. Just as additive models extend linear models by replacing linear functions with a set of one-dimensional smooth functions, the nonparanormal extends the normal by transforming the variables by smooth functions. We derive a method for estimating the nonparanormal, study the method's theoretical properties, and show that it works well in many examples.},
author = {Liu, Han and Lafferty, John and Wasserman, Larry},
doi = {10.1145/1577069.1755863},
journal = {Journal of Machine Learning Research},
month = apr,
shorttitle = {The {Nonparanormal}},
title = {The Nonparanormal: {Semiparametric} Estimation of High Dimensional Undirected Graphs},
volume = {10},
year = {2009}}
@article{magliacane_ancestral_2017,
abstract = {Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.},
annote = {Comment: In Proceedings of Advances in Neural Information Processing Systems 29 (NIPS 2016)},
author = {Magliacane, Sara and Claassen, Tom and Mooij, Joris M.},
doi = {10.48550/arXiv.1606.07035},
journal = {arXiv preprint arXiv:1606.07035},
title = {Ancestral Causal Inference},
urldate = {2023-02-17},
year = {2017},
bdsk-url-1 = {http://arxiv.org/abs/1606.07035},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.1606.07035}}
@article{malinsky_causal_2018,
abstract = {Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a ``user's guide'' to these methods, though not in the sense of specifying exact button presses in a software package. Instead, we explain the larger ``pipeline'' within which these methods are used and discuss key steps in moving from initial research idea to validated causal structure.},
annote = {e12470 10.1111/phc3.12470},
author = {Malinsky, Daniel and Danks, David},
doi = {10.1111/phc3.12470},
issn = {1747-9991},
journal = {Philosophy Compass},
language = {en},
number = {1},
pages = {e12470},
shorttitle = {Causal discovery algorithms},
title = {Causal discovery algorithms: {A} practical guide},
urldate = {2023-04-15},
volume = {13},
year = {2018},
bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1111/phc3.12470},
bdsk-url-2 = {https://doi.org/10.1111/phc3.12470}}
@article{mason_emotional_2020,
author = {Mason, Tyler B. and Dunton, Genevieve F. and Gearhardt, Ashley N. and Leventhal, Adam M.},
doi = {10.1016/j.eatbeh.2019.101343},
file = {PubMed Central Full Text PDF:/Users/Kyuri1/Zotero/storage/ZQKZJLX3/Mason et al. - 2020 - Emotional disorder symptoms, anhedonia, and negati.pdf:application/pdf},
issn = {1471-0153},
journal = {Eating behaviors},
month = jan,
pages = {101343},
pmcid = {PMC7044051},
pmid = {31715461},
title = {Emotional disorder symptoms, anhedonia, and negative urgency as predictors of hedonic hunger in adolescents},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044051/},
urldate = {2023-04-24},
volume = {36},
year = {2020},
bdsk-url-1 = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044051/},
bdsk-url-2 = {https://doi.org/10.1016/j.eatbeh.2019.101343}}
@article{mason_anhedonia_2021,
author = {Mason, Tyler B. and Smith, Kathryn E. and Anderson, Lisa M. and Hazzard, Vivienne M.},
doi = {10.1002/eat.23433},
file = {PubMed Central Full Text PDF:/Users/Kyuri1/Zotero/storage/K6HVWXLW/Mason et al. - 2021 - Anhedonia, Positive Affect Dysregulation, and Risk.pdf:application/pdf},
issn = {0276-3478},
journal = {The International journal of eating disorders},
month = mar,
number = {3},
pages = {287--292},
pmcid = {PMC8673784},
pmid = {33295671},
title = {Anhedonia, {Positive} {Affect} {Dysregulation}, and {Risk} and {Maintenance} of {Binge}-{Eating} {Disorder}},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673784/},
urldate = {2023-04-24},
volume = {54},
year = {2021},
bdsk-url-1 = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673784/},
bdsk-url-2 = {https://doi.org/10.1002/eat.23433}}
@article{menzies_systematic_2021,
author = {Menzies, Victoria and Kelly, Debra L and Yang, Gee S and Starkweather, Angela and Lyon, Debra E},
doi = {10.1177/1742395319836472},
file = {PubMed Central Full Text PDF:/Users/Kyuri1/Zotero/storage/VZI5WEAT/Menzies et al. - 2021 - A systematic review of the association between fat.pdf:application/pdf},
issn = {1742-3953},
journal = {Chronic illness},
month = jun,
number = {2},
pages = {129--150},
pmcid = {PMC6832772},
pmid = {30884965},
title = {A systematic review of the association between fatigue and cognition in chronic noncommunicable diseases},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832772/},
urldate = {2023-04-24},
volume = {17},
year = {2021},
bdsk-url-1 = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832772/},
bdsk-url-2 = {https://doi.org/10.1177/1742395319836472}}
@article{mcnally_co-morbid_2017,
author = {McNally, R. J. and Mair, P. and Mugno, B. L. and Riemann, B. C.},
doi = {10.1017/S0033291716003287},
issn = {1469-8978},
journal = {Psychological Medicine},
keywords = {Adolescent, Adult, Aged, Bayes Theorem, Co-morbidity, Comorbidity, depression, Depression, Depressive Disorder, Female, graphical LASSO, Humans, Male, Middle Aged, Models, Statistical, network analysis, Obsessive-Compulsive Disorder, obsessive--compulsive disorder, Young Adult},
language = {eng},
number = {7},
pages = {1204--1214},
pmid = {28052778},
shorttitle = {Co-morbid obsessive-compulsive disorder and depression},
title = {Co-morbid obsessive-compulsive disorder and depression: a {Bayesian} network approach},
volume = {47},
year = {2017},
bdsk-url-1 = {https://doi.org/10.1017/S0033291716003287}}
@article{mooij_joint_2020,
annote = {Comment: Final version, as published by JMLR},
author = {Mooij, Joris M. and Magliacane, Sara and Claassen, Tom},
doi = {10.48550/arXiv.1611.10351},
file = {arXiv Fulltext PDF:/Users/Kyuri1/Zotero/storage/D84CXP9Q/Mooij et al. - 2020 - Joint Causal Inference from Multiple Contexts.pdf:application/pdf;arXiv.org Snapshot:/Users/Kyuri1/Zotero/storage/QD4CA84N/1611.html:text/html},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
journal = {arXiv preprint arXiv:1611.10351},
title = {Joint Causal Inference from Multiple Contexts},
year = {2020},
bdsk-url-1 = {http://arxiv.org/abs/1611.10351},
bdsk-url-2 = {https://doi.org/10.48550/arXiv.1611.10351}}
@Inproceedings{mooij_classen2020,
title = {Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles},
author = {Mooij, Joris M. and Claassen, Tom},
booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence},
pages = {1159--1168},
year = {2020},
publisher = {Proceedings of Machine Learning Research},
pdf = {http://proceedings.mlr.press/v124/m-mooij20a/m-mooij20a.pdf}
}
@article{Mooijetal16,
title = {Distinguishing cause from effect using observational data: {Methods} and benchmarks},
author = {Mooij, J.M. and Peters, J. and Janzing, D. and Zscheischler, J. and Sch{\"o}lkopf, B.},
journal = {Journal of Machine Learning Research},
volume = {17},
number = {32},
pages = {1-102},
year = {2016},
doi = {},
url = {http://jmlr.org/papers/volume17/14-518/14-518.pdf}
}
@inproceedings{pearl_causal_2010,
abstract = {This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in Pearl (2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.},
author = {Pearl, Judea},
booktitle = {Proceedings of {Workshop} on {Causality}: {Objectives} and {Assessment} at {NIPS} 2008},
language = {en},
pages = {39--58},
publisher = {PMLR},
title = {Causal Inference},
year = {2010},
bdsk-url-1 = {https://proceedings.mlr.press/v6/pearl10a.html}}
@book{pearl2009causality,
title={Causality},
author={Pearl, Judea},
year={2009},
publisher={Cambridge university press}
}
@article{pek_how_2018,
author = {Pek, Jolynn and Wong, Octavia and Wong, Augustine C. M.},
issn = {1664-1078},
journal = {Frontiers in Psychology},
shorttitle = {How to {Address} {Non}-normality},
title = {How to Address Non-normality: {A} Taxonomy of Approaches, Reviewed, and Illustrated},
volume = {9},
year = {2018},
doi = {10.3389/fpsyg.2018.02104},
bdsk-url-1 = {https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02104}}
@book{pearl_probabilistic_1988,
abstract = {Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.},
author = {Pearl, Judea},
isbn = {978-1-55860-479-7},
keywords = {Computers / Artificial Intelligence / General},
language = {en},
month = sep,
publisher = {Morgan Kaufmann},
shorttitle = {Probabilistic {Reasoning} in {Intelligent} {Systems}},
title = {Probabilistic {Reasoning} in {Intelligent} {Systems}: {Networks} of {Plausible} {Inference}},
year = {1988}}
@book{peters_elements_2017,
author = {Peters, Jonas and Janzing, Dominik and Sch\"{o}lkopf, Bernhard},
isbn = {978-0-262-03731-0},
publisher = {MIT Press},
shorttitle = {Elements of Causal Inference},
title = {Elements of causal inference: {Foundations} and learning algorithms},
year = {2017}}
@article{peters_causal_2016,
abstract = {What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance of a prediction under a causal model for causal inference: given different experimental settings (e.g. various interventions) we collect all models that do show invariance in their predictive accuracy across settings and interventions. The causal model will be a member of this set of models with high probability. This approach yields valid confidence intervals for the causal relationships in quite general scenarios. We examine the example of structural equation models in more detail and provide sufficient assumptions under which the set of causal predictors becomes identifiable. We further investigate robustness properties of our approach under model misspecification and discuss possible extensions. The empirical properties are studied for various data sets, including large-scale gene perturbation experiments.},
author = {Peters, Jonas and B{\"u}hlmann, Peter and Meinshausen, Nicolai},
doi = {10.1111/rssb.12167},
file = {Full Text PDF:/Users/Kyuri1/Zotero/storage/JY32FTH2/Peters et al. - 2016 - Causal Inference by using Invariant Prediction Id.pdf:application/pdf},
issn = {1369-7412},
journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
month = nov,
number = {5},
pages = {947--1012},
shorttitle = {Causal {Inference} by using {Invariant} {Prediction}},
title = {Causal {Inference} by using {Invariant} {Prediction}: {Identification} and {Confidence} {Intervals}},
url = {https://doi.org/10.1111/rssb.12167},
urldate = {2023-05-03},
volume = {78},
year = {2016},
bdsk-url-1 = {https://doi.org/10.1111/rssb.12167}}
@techreport{richardson1996,
title={Discovering cyclic causal structure},
author={Thomas Richardson},
number = {CMU-PHIL-68},
year = {1996},
institution = {Carnegie Mellon University, Department of Philosophy},
month = {February},
address = {Pittsburgh, Pennsylvania}
}
@incollection{richardson1996automated,
author = {Richardson, Thomas and Spirtes, Peter},
title = {Automated discovery of linear feedback models},
editor = {Glymour, Clark and Cooper, Gregory F.},
booktitle = {Computation, causation, and discovery},
year = {1999},
chapter = {7},
publisher = {MIT Press},
isbn = {9780262315821},
pages = {253 -- 302},
doi = {10.7551/mitpress/2006.001.0001}
}
@inproceedings{Richardson1996a,
author = {Thomas Richardson},
title = {A Discovery Algorithm for Directed Cyclic Graphs},
year = {1996},
isbn = {155860412X},
publisher = {Morgan Kaufmann Publishers Inc.},
booktitle = {Proceedings of the Twelfth International Conference on Uncertainty in Artificial Intelligence},
pages = {454–461},
numpages = {8},
location = {Portland, OR},
}
@article{Richardson2002,
author = {Thomas Richardson and Peter Spirtes},
doi = {10.1214/aos/1031689015},
journal = {The Annals of Statistics},
keywords = {$m$-separation, ancestral graph, DAG, data-generating process, Directed acyclic graph, latent variable, marginalizing and conditioning, MC-graph, path diagram, summary graph},
number = {4},
pages = {962 -- 1030},
publisher = {Institute of Mathematical Statistics},
title = {{Ancestral graph Markov models}},
volume = {30},
year = {2002},
bdsk-url-1 = {https://doi.org/10.1214/aos/1031689015}}
@article{richardson_markov_2003,
author = {Richardson, Thomas},
doi = {10.1111/1467-9469.00323},
file = {Full Text PDF:/Users/Kyuri1/Zotero/storage/2M9KI5KX/Richardson - 2003 - Markov Properties for Acyclic Directed Mixed Graph.pdf:application/pdf},
issn = {1467-9469},
journal = {Scandinavian Journal of Statistics},
keywords = {acyclic directed mixed graph, covariance graph, graphical model, local Markov property, path diagram, summary graph},
language = {en},
number = {1},
pages = {145--157},
title = {Markov Properties for Acyclic Directed Mixed Graphs},
volume = {30},
year = {2003},
bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-9469.00323},
bdsk-url-2 = {https://doi.org/10.1111/1467-9469.00323}}