The Challenge of Generating Causal Hypotheses Using Network Models

References

• Abadir, K. M., & Magnus, J. R. (2005). Matrix algebra (Vol. 1). Cambridge University Press.
   Google Scholar
• Armour, C., Fried, E. I., Deserno, M. K., Tsai, J., & Pietrzak, R. H. (2017). A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in us military veterans. Journal of Anxiety Disorders, 45, 49–59. https://doi.org/10.1016/j.janxdis.2016.11.008
   PubMed Web of Science ®Google Scholar
• Betz, L. T., Penzel, N., Rosen, M., & Kambeitz, J. (2021). Relationships between childhood trauma and perceived stress in the general population: a network perspective. Psychological Medicine, 51, 2696–2706. https://doi.org/10.1017/S003329172000135X
   PubMed Web of Science ®Google Scholar
• Black, L., Panayiotou, M., & Humphrey, N. (2021). Internalizing symptoms, well-being, and correlates in adolescence: A multiverse exploration via cross-lagged panel network models. Development and Psychopathology, 1–15. https://doi.org/10.1017/S0954579421000225
   Google Scholar
• Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons.
   Google Scholar
• Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In Handbook of causal analysis for social research (pp. 301–328). Springer.
   Google Scholar
• Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16, 5–13. https://doi.org/10.1002/wps.20375
   PubMed Web of Science ®Google Scholar
• Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
   PubMed Web of Science ®Google Scholar
• Borsboom, D., Cramer, A. O., Schmittmann, V. D., Epskamp, S., & Waldorp, L. J. (2011). The small world of psychopathology. PLOS One, 6, e27407.
   PubMed Web of Science ®Google Scholar
• Boschloo, L., Schoevers, R. A., Borkulo, C. D., van Borsboom, D., & Oldehinkel, A. J. (2016). The network structure of psychopathology in a community sample of preadolescents. Journal of Abnormal Psychology, 125, 599–606. https://doi.org/10.1037/abn0000150
   PubMed Web of Science ®Google Scholar
• Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128, 892–903. https://doi.org/10.1037/abn0000446
   PubMed Web of Science ®Google Scholar
• Buonaccorsi, J. P. (2010). Measurement error, models, methods, and applications. Chapman & Hall/CRC.
   Google Scholar
• Colombo, D., Maathuis, M. H., Kalisch, M., & Richardson, T. S. (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables. The Annals of Statistics, 40, 294–321. https://doi.org/10.1214/11-AOS940
   Web of Science ®Google Scholar
• Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13–29. https://doi.org/10.1016/j.jrp.2014.07.003
   Web of Science ®Google Scholar
• Cox, D. R., & Wermuth, N. (1996). Multivariate dependencies: Models, analysis and interpretation. Chapman and Hall/CRC.
   Google Scholar
• Cramer, A. O. J., van Borkulo, C. D., Giltay, E. J., van der Maas, H. L. J., Kendler, K. S., Scheffer, M., & Borsboom, D. (2016). Major depression as a complex dynamic system. PLOS One, 11, e0167490. https://doi.org/10.1371/journal.pone.0167490
   PubMed Web of Science ®Google Scholar
• Cramer, A. O., Waldorp, L. J., van der Maas, H. L. J., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 332, 137–150.
   Google Scholar
• Dablander, F., & Hinne, M. (2019). Node centrality measures are a poor substitute for causal inference. Scientific Reports, 9, 6846.
   PubMed Web of Science ®Google Scholar
• Dalege, J., Borsboom, D., van Harreveld, F., van den Berg, H., Conner, M., & Maas, H. L. (2016). Toward a formalized account of attitudes: The causal attitude network (can) model. Psychological Review, 123, 2–22. https://doi.org/10.1037/a0039802
   PubMed Web of Science ®Google Scholar
• Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society. Series B (Methodological), 41, 1–31. https://doi.org/10.1111/j.2517-6161.1979.tb01052.x
   Web of Science ®Google Scholar
• Dempster, A. P. (1972). Covariance selection. Biometrics, 28, 157–175. https://doi.org/10.2307/2528966
   Web of Science ®Google Scholar
• Deserno, M. K., Borsboom, D., Begeer, S., & Geurts, H. M. (2017). Multicausal systems ask for multicausal approaches: A network perspective on subjective well-being in individuals with autism spectrum disorder. Autism: The International Journal of Research and Practice, 21, 960–971. https://doi.org/10.1177/1362361316660309
   PubMed Web of Science ®Google Scholar
• Eberhardt, F. (2017). Introduction to the foundations of causal discovery. International Journal of Data Science and Analytics, 3, 81–91. https://doi.org/10.1007/s41060-016-0038-6
   Google Scholar
• Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A.-M., Riese, H., & Cramer, A. O. J. (2018). Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections. Clinical Psychological Science, 6, 416–427. https://doi.org/10.1177/2167702617744325
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195–212.
   PubMed Web of Science ®Google Scholar
• Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617–634.
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82, 904–927.
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53, 453–480.
   PubMed Web of Science ®Google Scholar
• Forré, P., & Mooij, J. M. (2018). Constraint-based causal discovery for non-linear structural causal models with cycles and latent confounders. arXiv preprint arXiv:1807.03024.
   Google Scholar
• Fried, E. I., Bockting, C., Arjadi, R., Borsboom, D., Amshoff, M., Cramer, A. O. J., Epskamp, S., Tuerlinckx, F., Carr, D., & Stroebe, M. (2015). From loss to loneliness: The relationship between bereavement and depressive symptoms. Journal of Abnormal Psychology, 124, 256–265. https://doi.org/10.1037/abn0000028
   PubMed Web of Science ®Google Scholar
• Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016). What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314–320. https://doi.org/10.1016/j.jad.2015.09.005
   PubMed Web of Science ®Google Scholar
• Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432–441.
   PubMed Web of Science ®Google Scholar
• Ghoshal, A., & Honorio, J. (2017). Learning linear structural equation models in polynomial time and sample complexity. arXiv preprint arXiv:1707.04673.
   Google Scholar
• Glymour, C., Zhang, K., & Spirtes, P. (2019). Review of causal discovery methods based on graphical models. Frontiers in Genetics, 10, 524.
   PubMed Web of Science ®Google Scholar
• Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLOS One, 12, e0174035.
   PubMed Web of Science ®Google Scholar
• Greenland, S. (2003). Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology, 14, 300–306.
   PubMed Web of Science ®Google Scholar
• Haslbeck, J. M. B., & Ryan, O. (2021). Recovering within-person dynamics from psychological time series. Multivariate Behavioral Research, 1–32. https://doi.org/10.1080/00273171.2021.1896353
   Web of Science ®Google Scholar
• Haslbeck, J. M. B., Ryan, O., & Dablander, F. (2021a). The sum of all fears: Comparing networks based on symptom sum-scores. Psychological Methods. https://doi.org/10.1037/met0000418
   Google Scholar
• Haslbeck, J. M. B., Ryan, O., Robinaugh, D. J., Waldorp, L. J., & Borsboom, D. (2021b). Modeling psychopathology: From data models to formal theories. Psychological Methods. https://doi.org/10.1037/met0000303
   Web of Science ®Google Scholar
• Haslbeck, J. M. B., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50, 853–861.
   PubMed Web of Science ®Google Scholar
• Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Structure estimation for time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software, 93, 1–46. https://doi.org/10.18637/jss.v093.i08
   Web of Science ®Google Scholar
• Hernan, M. A., & Robins, J. M. (2010). Causal inference. CRC.
   Google Scholar
• Hoffman, M., Steinley, D., Trull, T. J., Lane, S. P., Wood, P. K., & Sher, K. J. (2019). The influence of sample selection on the structure of psychopathology symptom networks: An example with alcohol use disorder. Journal of Abnormal Psychology, 128, 473–486.
   PubMed Web of Science ®Google Scholar
• Hornik, K. (2012). The comprehensive r archive network. WIREs Computational Statistics, 4, 394–398. https://doi.org/10.1002/wics.1212
   Google Scholar
• Hyttinen, A., Eberhardt, F., & Hoyer, P. O. (2012). Learning linear cyclic causal models with latent variables. The Journal of Machine Learning Research, 13, 3387–3439.
   Web of Science ®Google Scholar
• Isvoranu, A.-M., van Borkulo, C. D., Boyette, L.-L., Wigman, J. T., Vinkers, C. H., & Borsboom, D. (2017). A network approach to psychosis: pathways between childhood trauma and psychotic symptoms. Schizophrenia Bulletin, 43, 187–196. https://doi.org/10.1093/schbul/sbw055
   PubMed Web of Science ®Google Scholar
• Isvoranu, A.-M., Guloksuz, S., Epskamp, S., van Os, J., Borsboom, D., & Investigators, G. (2020). Toward incorporating genetic risk scores into symptom networks of psychosis. Psychological Medicine, 50, 636–643. https://doi.org/10.1017/S003329171900045X
   PubMed Web of Science ®Google Scholar
• Jiang, Z., & Ding, P. (2017). The directions of selection bias. Statistics & Probability Letters, 125, 104–109. https://doi.org/10.1016/j.spl.2017.01.022
   Web of Science ®Google Scholar
• Kossakowski, J. J., Waldorp, L. J., & van der Maas, H. L. (2021). The search for causality: A comparison of different techniques for causal inference graphs. Psychological Methods, 26, 719–742. https://doi.org/10.1037/met0000390
   PubMed Web of Science ®Google Scholar
• Lacerda, G., Spirtes, P. L., Ramsey, J., & Hoyer, P. O. (2012). Discovering cyclic causal models by independent components analysis. arXiv preprint arXiv:1206.3273.
   Google Scholar
• Lauritzen, S. L. (1996). Graphical models (Vol. 17). Clarendon Press.
   Google Scholar
• Levina, E., Rothman, A., & Zhu, J. (2008). Sparse estimation of large covariance matrices via a nested lasso penalty. The Annals of Applied Statistics, 2, 245–263. https://doi.org/10.1214/07-AOAS139
   Web of Science ®Google Scholar
• MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185–199.
   PubMed Web of Science ®Google Scholar
• McNally, R. J., Robinaugh, D. J., Wu, G. W., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3, 836–849. https://doi.org/10.1177/2167702614553230
   Web of Science ®Google Scholar
• Mooij, J. M., Magliacane, S., & Claassen, T. (2016). Joint causal inference from multiple contexts. arXiv preprint arXiv:1611.10351.
   Google Scholar
• Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., & Schölkopf, B. (2016). Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research, 17, 1103–1204.
   Web of Science ®Google Scholar
• Nguyen, T., Dafoe, A., & Ogburn, E. (2019). The magnitude and direction of collider bias for binary variables. Epidemiologic Methods, 8, 20170013. https://doi.org/10.1515/em-2017-0013
   Google Scholar
• Pearl, J. (2009). Causality. Cambridge University Press.
   Google Scholar
• Pearl, J. (2013). Linear models: A useful “microscope” for causal analysis. Journal of Causal Inference, 1, 155–170. https://doi.org/10.1515/jci-2013-0003
   Google Scholar
• Pearl, J., & Dechter, R. (1996). Identifying independencies in causal graphs with feedback. arXiv preprint arXiv:1302.3595.
   Google Scholar
• Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. John Wiley & Sons.
   Google Scholar
• Peters, J., Bühlmann, P., & Meinshausen, N. (2016). Causal inference by using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78, 947–1012. https://doi.org/10.1111/rssb.12167
   Web of Science ®Google Scholar
• Peters, J., Janzing, D., & Schölkopf, B. (2010). Identifying cause and effect on discrete data using additive noise models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (pp. 597–604).
   Google Scholar
• Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference: foundations and learning algorithms. The MIT Press.
   Google Scholar
• R Core Team (2021). R: A language and environment for statistical computing [Computer software manual]. Retrieved from https://www.R-project.org/
   Google Scholar
• Ramsey, J., Glymour, M., Sanchez-Romero, R., & Glymour, C. (2017). A million variables and more: The fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. International Journal of Data Science and Analytics, 3, 121–129.
   PubMedGoogle Scholar
• Raykov, T., & Marcoulides, G. A. (2001). Can there be infinitely many models equivalent to a given covariance structure model? Structural Equation Modeling: A Multidisciplinary Journal, 8, 142–149. https://doi.org/10.1207/S15328007SEM0801_8
   Web of Science ®Google Scholar
• Richardson, T., & Spirtes, P. (2002). Ancestral graph Markov models. The Annals of Statistics, 30, 962–1030. https://doi.org/10.1214/aos/1031689015
   Web of Science ®Google Scholar
• Robinaugh, D. J., Haslbeck, J., Ryan, O., Fried, E. I., & Waldorp, L. J. (2021). Invisible hands and fine calipers: A call to use formal theory as a toolkit for theory construction. Perspectives on Psychological Science, 16, 725–743. https://doi.org/10.1177/1745691620974697
   Web of Science ®Google Scholar
• Robinaugh, D. J., Hoekstra, R. H., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research. Psychological Medicine, 50, 353–366. https://doi.org/10.1017/S0033291719003404
   PubMed Web of Science ®Google Scholar
• Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125, 747–757.
   PubMed Web of Science ®Google Scholar
• Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1, 27–42. https://doi.org/10.1177/2515245917745629
   Google Scholar
• Scheines, R., Spirtes, P., Glymour, C., Meek, C., & Richardson, T. (1998). The tetrad project: Constraint based aids to causal model specification. Multivariate Behavioral Research, 33, 65–117. https://doi.org/10.1207/s15327906mbr3301_3
   PubMed Web of Science ®Google Scholar
• Schuurman, N. K., & Hamaker, E. L. (2019). Measurement error and person-specific reliability in multilevel autoregressive modeling. Psychological Methods, 24, 70–91.
   PubMed Web of Science ®Google Scholar
• Shimizu, S. (2014). Lingam: Non-gaussian methods for estimating causal structures. Behaviormetrika, 41, 65–98. https://doi.org/10.2333/bhmk.41.65
   Google Scholar
• Shimizu, S., Hoyer, P. O., Hyvärinen, A., Kerminen, A., & Jordan, M. (2006). A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7, 2003–2030.
   Web of Science ®Google Scholar
• Shojaie, A., & Michailidis, G. (2010). Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs. Biometrika, 97, 519–538.
   PubMed Web of Science ®Google Scholar
• Spector, P. E., & Brannick, M. T. (2011). Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods, 14, 287–305. https://doi.org/10.1177/1094428110369842
   Web of Science ®Google Scholar
• Spirtes, P. (1994). Conditional independence in directed cyclic graphical models for feedback.
   Google Scholar
• Spirtes, P. (1995). Directed cyclic graphical representations of feedback models. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 491–498).
   Google Scholar
• Spirtes, P., Glymour, C. N., Scheines, R., Heckerman, D., Meek, C., & Cooper, G. (2000). Causation, prediction, and search. MIT Press.
   Google Scholar
• Spirtes, P., Meek, C., & Richardson, T. (1995). Causal inference in the presence of latent variables and selection bias. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 499–506).
   Google Scholar
• Strobl, E. V. (2019). A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias. International Journal of Data Science and Analytics, 8, 33–56. https://doi.org/10.1007/s41060-018-0158-2
   Google Scholar
• Tomarken, A. J., & Waller, N. G. (2003). Potential problems with “well fitting” models. Journal of Abnormal Psychology, 112, 578–598.
   PubMed Web of Science ®Google Scholar
• Tzelgov, J., & Henik, A. (1991). Suppression situations in psychological research: Definitions, implications, and applications. Psychological Bulletin, 109, 524–536. https://doi.org/10.1037/0033-2909.109.3.524
   Web of Science ®Google Scholar
• Uhler, C., Raskutti, G., Bühlmann, P., & Yu, B. (2013). Geometry of the faithfulness assumption in causal inference. The Annals of Statistics, 41, 436–463.
   Web of Science ®Google Scholar
• van Bork, R., Epskamp, S., Rhemtulla, M., Borsboom, D., & van der Maas, H. L. (2017). What is the p-factor of psychopathology? Some risks of general factor modeling. Theory & Psychology, 27, 759–773. https://doi.org/10.1177/0959354317737185
   Web of Science ®Google Scholar
• van Borkulo, C., Boschloo, L., Borsboom, D., Penninx, B. W., Waldorp, L. J., & Schoevers, R. A. (2015). Association of symptom network structure with the course of [corrected] depression. JAMA Psychiatry, 72, 1219–1226.
   PubMed Web of Science ®Google Scholar
• van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4, 5918. https://doi.org/10.1038/srep05918
   PubMed Web of Science ®Google Scholar
• Van Der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113, 842–861.
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J., & Robins, J. M. (2007). Directed acyclic graphs, sufficient causes, and the properties of conditioning on a common effect. American Journal of Epidemiology, 166, 1096–1104.
   PubMed Web of Science ®Google Scholar
• Waldorp, L., Kossakowski, J., & van der Maas, H. L. (2021). Perturbation graphs, invariant prediction and causal relations in psychology. arXiv preprint arXiv:2109.00404.
   Google Scholar
• Wermuth, N., & Lauritzen, S. L. (1983). Graphical and recursive models for contingency tables. Biometrika, 70, 537–552. https://doi.org/10.2307/2336490
   Web of Science ®Google Scholar
• Williams, D. R., & Mulder, J. (2020a). Bayesian hypothesis testing for gaussian graphical models: Conditional independence and order constraints. Journal of Mathematical Psychology, 99, 102441. https://doi.org/10.1016/j.jmp.2020.102441
   Web of Science ®Google Scholar
• Williams, D. R., & Mulder, J. (2020b). Bggm: Bayesian gaussian graphical models in R. Journal of Open Source Software, 5, 2111. https://doi.org/10.21105/joss.02111
   Google Scholar
• Williams, D. R., & Rast, P. (2020). Back to the basics: Rethinking partial correlation network methodology. British Journal of Mathematical and Statistical Psychology, 73, 187–212.
   PubMed Web of Science ®Google Scholar
• Williams, D. R., Rhemtulla, M., Wysocki, A. C., & Rast, P. (2019). On nonregularized estimation of psychological networks. Multivariate Behavioral Research, 54, 719–750.
   PubMed Web of Science ®Google Scholar
• Zhang, J. (2008). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172, 1873–1896. https://doi.org/10.1016/j.artint.2008.08.001
   Web of Science ®Google Scholar