{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:38Z","timestamp":1740108098772,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00521-024-09833-5","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T13:03:07Z","timestamp":1715605387000},"page":"15075-15090","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["What if we intervene?: Higher-order cross-lagged causal model with interventional approach under observational design"],"prefix":"10.1007","volume":"36","author":[{"given":"Christopher","family":"Castro","sequence":"first","affiliation":[]},{"given":"Kevin","family":"Michell","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5878-072X","authenticated-orcid":false,"given":"Werner","family":"Kristjanpoller","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6408-7107","authenticated-orcid":false,"given":"Marcel C.","family":"Minutolo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"issue":"18","key":"9833_CR1","doi-asserted-by":"publisher","first-page":"13631","DOI":"10.1007\/s00521-023-08397-0","volume":"35","author":"C Bermeo","year":"2023","unstructured":"Bermeo C, Michell K, Kristjanpoller W (2023) Estimation of causality in economic growth and expansionary policies using uplift modeling. Neural Comput Appl 35(18):13631\u201313645. https:\/\/doi.org\/10.1007\/s00521-023-08397-0","journal-title":"Neural Comput Appl"},{"key":"9833_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2022.102859","volume":"78","author":"Y Hong","year":"2022","unstructured":"Hong Y, Ma F, Wang L et al (2022) How does the COVID-19 outbreak affect the causality between gold and the stock market? New evidence from the extreme granger causality test. Resour Policy 78:102859. https:\/\/doi.org\/10.1016\/j.resourpol.2022.102859","journal-title":"Resour Policy"},{"key":"9833_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2022.103073","volume":"49","author":"V Fromentin","year":"2022","unstructured":"Fromentin V, Lorraine M, Ariane C et al (2022) Time-varying causality between stock prices and macroeconomic fundamentals: connection or disconnection? Finance Res Lett 49:103073. https:\/\/doi.org\/10.1016\/j.frl.2022.103073","journal-title":"Finance Res Lett"},{"key":"9833_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2022.103124","volume":"49","author":"CF Chen","year":"2022","unstructured":"Chen CF, Chiang Sh (2022) Portfolio diversification possibilities between the stock and housing markets in G7 countries: evidence from the time-varying granger causality. Finance Res Lett 49:103124. https:\/\/doi.org\/10.1016\/j.frl.2022.103124","journal-title":"Finance Res Lett"},{"issue":"13","key":"9833_CR5","doi-asserted-by":"publisher","first-page":"9841","DOI":"10.1007\/s00521-023-08221-9","volume":"35","author":"W Kristjanpoller","year":"2023","unstructured":"Kristjanpoller W, Michell K, Olson J (2023) Determining the gender wage gap through causal inference and machine learning models: evidence from Chile. Neural Comput Appl 35(13):9841\u20139863. https:\/\/doi.org\/10.1007\/s00521-023-08221-9","journal-title":"Neural Comput Appl"},{"key":"9833_CR6","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jad.2019.02.020","volume":"249","author":"Y Shi","year":"2019","unstructured":"Shi Y, Liu W, Liu R et al (2019) Investigation of the emotional network in depression after stroke: a study of multivariate granger causality analysis of fMRI data. J Affect Disord 249:35\u201344. https:\/\/doi.org\/10.1016\/j.jad.2019.02.020","journal-title":"J Affect Disord"},{"key":"9833_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.paid.2022.111729","volume":"196","author":"R Tsukawaki","year":"2022","unstructured":"Tsukawaki R, Imura T, Hirakawa M (2022) Causal relationship between the two dimensions of self-directed humor and depression, anxiety, and subjective well-being: a longitudinal study. Personal Individ Differ 196:111729. https:\/\/doi.org\/10.1016\/j.paid.2022.111729","journal-title":"Personal Individ Differ"},{"key":"9833_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2021.100992","volume":"38","author":"Z Ye","year":"2021","unstructured":"Ye Z, Kappelmann N, Moser S et al (2021) Role of inflammation in depression and anxiety: tests for disorder specificity, linearity and potential causality of association in the UK biobank. EClinicalMedicine 38:100992. https:\/\/doi.org\/10.1016\/j.eclinm.2021.100992","journal-title":"EClinicalMedicine"},{"issue":"4","key":"9833_CR9","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.cjca.2022.01.016","volume":"38","author":"M Samuel","year":"2022","unstructured":"Samuel M, Elsokkari I, Sapp JL (2022) Ventricular tachycardia burden and mortality: association or causality? Can J Cardiol 38(4):454\u2013464. https:\/\/doi.org\/10.1016\/j.cjca.2022.01.016","journal-title":"Can J Cardiol"},{"key":"9833_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119496","volume":"260","author":"B Soleimani","year":"2022","unstructured":"Soleimani B, Das P, Karunathilake ID et al (2022) NLGC: network localized granger causality with application to MEG directional functional connectivity analysis. Neuroimage 260:119496. https:\/\/doi.org\/10.1016\/j.neuroimage.2022.119496","journal-title":"Neuroimage"},{"issue":"11","key":"9833_CR11","doi-asserted-by":"publisher","first-page":"2086","DOI":"10.1016\/j.ajhg.2021.09.011","volume":"108","author":"HM Tanha","year":"2021","unstructured":"Tanha HM, Sathyanarayanan A, Nyholt DR (2021) Genetic overlap and causality between blood metabolites and migraine. Am J Hum Genet 108(11):2086\u20132098. https:\/\/doi.org\/10.1016\/j.ajhg.2021.09.011","journal-title":"Am J Hum Genet"},{"key":"9833_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118036","volume":"207","author":"Z Yin","year":"2022","unstructured":"Yin Z, Barucca P (2022) Deep recurrent modelling of granger causality with latent confounding. Expert Syst Appl 207:118036. https:\/\/doi.org\/10.1016\/j.eswa.2022.118036","journal-title":"Expert Syst Appl"},{"key":"9833_CR13","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.specom.2022.07.005","volume":"145","author":"JX Ye","year":"2022","unstructured":"Ye JX, Wen XC, Wang XZ et al (2022) GM-TCNet: gated multi-scale temporal convolutional network using emotion causality for speech emotion recognition. Speech Commun 145:21\u201335. https:\/\/doi.org\/10.1016\/j.specom.2022.07.005","journal-title":"Speech Commun"},{"key":"9833_CR14","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.neucom.2020.08.078","volume":"423","author":"Z Li","year":"2021","unstructured":"Li Z, Li Q, Zou X et al (2021) Causality extraction based on self-attentive BiLSTM-CRF with transferred embeddings. Neurocomputing 423:207\u2013219. https:\/\/doi.org\/10.1016\/j.neucom.2020.08.078","journal-title":"Neurocomputing"},{"issue":"9","key":"9833_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e10697","volume":"8","author":"TS Adebayo","year":"2022","unstructured":"Adebayo TS, Ibrahim RL, Agyekum EB et al (2022) The environmental aspects of renewable energy consumption and structural change in Sweden: a new perspective from wavelet-based granger causality approach. Heliyon 8(9):e10697. https:\/\/doi.org\/10.1016\/j.heliyon.2022.e10697","journal-title":"Heliyon"},{"issue":"59","key":"9833_CR16","doi-asserted-by":"publisher","first-page":"88908","DOI":"10.1007\/s11356-022-21602-2","volume":"29","author":"C Castro","year":"2022","unstructured":"Castro C, Kristjanpoller W, Olson JE (2022) Long-run economic and social determinants of the ecological footprint of Latin America: a panel causality approach. Environ Sci Pollut Res 29(59):88908\u201388924. https:\/\/doi.org\/10.1007\/s11356-022-21602-2","journal-title":"Environ Sci Pollut Res"},{"issue":"22","key":"9833_CR17","doi-asserted-by":"publisher","first-page":"16381","DOI":"10.1007\/s00521-023-08506-z","volume":"35","author":"S Shan","year":"2023","unstructured":"Shan S, Wang Y, Xie X et al (2023) Analysis of regional climate variables by using neural granger causality. Neural Comput Appl 35(22):16381\u201316402. https:\/\/doi.org\/10.1007\/s00521-023-08506-z","journal-title":"Neural Comput Appl"},{"key":"9833_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.140741","volume":"743","author":"Z Wang","year":"2020","unstructured":"Wang Z, Bui Q, Zhang B et al (2020) Biomass energy production and its impacts on the ecological footprint: an investigation of the G7 countries. Sci Total Environ 743:140741. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.140741","journal-title":"Sci Total Environ"},{"key":"9833_CR19","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/0165-1889(80)90069-X","volume":"2","author":"CW Granger","year":"1980","unstructured":"Granger CW (1980) Testing for causality: a personal viewpoint. J Econ Dyn Control 2:329\u2013352. https:\/\/doi.org\/10.1016\/0165-1889(80)90069-X","journal-title":"J Econ Dyn Control"},{"issue":"1","key":"9833_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1037\/a0015917","volume":"15","author":"SG West","year":"2010","unstructured":"West SG, Thoemmes F (2010) Campbell\u2019s and Rubin\u2019s perspectives on causal inference. Psychol Methods 15(1):18. https:\/\/doi.org\/10.1037\/a0015917","journal-title":"Psychol Methods"},{"key":"9833_CR21","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/978-3-030-16759-2_19","volume-title":"Handbook of research ethics and scientific integrity","author":"J Lewis","year":"2020","unstructured":"Lewis J (2020) Experimental design: ethics, integrity, and the scientific method. In: Handbook of research ethics and scientific integrity. Springer, Cham, pp 459\u2013474. https:\/\/doi.org\/10.1007\/978-3-030-16759-2_19"},{"issue":"3","key":"9833_CR22","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1080\/10705511.2020.1780598","volume":"28","author":"C Gische","year":"2021","unstructured":"Gische C, West SG, Voelkle MC (2021) Forecasting causal effects of interventions versus predicting future outcomes. Struct Equ Model 28(3):475\u2013492. https:\/\/doi.org\/10.1080\/10705511.2020.1780598","journal-title":"Struct Equ Model"},{"key":"9833_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcn.2020.100867","volume":"46","author":"EL Hamaker","year":"2020","unstructured":"Hamaker EL, Mulder JD, van IJzendoorn MH (2020) Description, prediction and causation: methodological challenges of studying child and adolescent development. Dev Cogn Neurosci 46:100867. https:\/\/doi.org\/10.1016\/j.dcn.2020.100867","journal-title":"Dev Cogn Neurosci"},{"issue":"1","key":"9833_CR24","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/s11336-021-09767-0","volume":"87","author":"O Ryan","year":"2022","unstructured":"Ryan O, Hamaker EL (2022) Time to intervene: a continuous-time approach to network analysis and centrality. Psychometrika 87(1):214\u2013252. https:\/\/doi.org\/10.1007\/s11336-021-09767-0","journal-title":"Psychometrika"},{"issue":"3","key":"9833_CR25","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1037\/met0000429","volume":"28","author":"MJ Vowels","year":"2023","unstructured":"Vowels MJ (2023) Misspecification and unreliable interpretations in psychology and social science. Psychol Methods 28(3):507","journal-title":"Psychol Methods"},{"issue":"7","key":"9833_CR26","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1038\/s42256-020-0197-y","volume":"2","author":"M Prosperi","year":"2020","unstructured":"Prosperi M, Guo Y, Sperrin M et al (2020) Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nat Mach Intell 2(7):369\u2013375. https:\/\/doi.org\/10.1038\/s42256-020-0197-y","journal-title":"Nat Mach Intell"},{"key":"9833_CR27","unstructured":"Flores CA et al (2007) Estimation of dose-response functions and optimal doses with a continuous treatment. University of Miami, Department of Economics, November"},{"issue":"4","key":"9833_CR28","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.jval.2019.01.006","volume":"22","author":"P Arora","year":"2019","unstructured":"Arora P, Boyne D, Slater JJ et al (2019) Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health 22(4):439\u2013445. https:\/\/doi.org\/10.1016\/j.jval.2019.01.006","journal-title":"Value Health"},{"issue":"1","key":"9833_CR29","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1089\/big.2017.0104","volume":"6","author":"F Devriendt","year":"2018","unstructured":"Devriendt F, Moldovan D, Verbeke W (2018) A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: a stepping stone toward the development of prescriptive analytics. Big data 6(1):13\u201341. https:\/\/doi.org\/10.1089\/big.2017.0104","journal-title":"Big data"},{"key":"9833_CR30","doi-asserted-by":"publisher","first-page":"2467","DOI":"10.1007\/s10618-021-00797-x","volume":"35","author":"K Kuang","year":"2021","unstructured":"Kuang K, Li Y, Li B et al (2021) Continuous treatment effect estimation via generative adversarial de-confounding. Data Min Knowl Disc 35:2467\u20132497. https:\/\/doi.org\/10.1007\/s10618-021-00797-x","journal-title":"Data Min Knowl Disc"},{"key":"9833_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107241","volume":"104","author":"W Kristjanpoller","year":"2021","unstructured":"Kristjanpoller W, Michell K, Minutolo MC (2021) A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19. Appl Soft Comput 104:107241. https:\/\/doi.org\/10.1016\/j.asoc.2021.107241","journal-title":"Appl Soft Comput"},{"issue":"7","key":"9833_CR32","first-page":"557","volume":"20","author":"S Wright","year":"1921","unstructured":"Wright S (1921) Correlation and causation. J Agric Res 20(7):557","journal-title":"J Agric Res"},{"key":"9833_CR33","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751","volume-title":"Causal inference in statistics, social, and biomedical sciences","author":"GW Imbens","year":"2015","unstructured":"Imbens GW, Rubin DB (2015) Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, Cambridge. https:\/\/doi.org\/10.1017\/CBO9781139025751"},{"key":"9833_CR34","doi-asserted-by":"publisher","first-page":"47","DOI":"10.2307\/1924332","volume":"60","author":"O Ashenfelter","year":"1978","unstructured":"Ashenfelter O (1978) Estimating the effect of training programs on earnings. Rev Econ Stat 60:47\u201357. https:\/\/doi.org\/10.2307\/1924332","journal-title":"Rev Econ Stat"},{"issue":"8","key":"9833_CR35","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1175\/JCLI-D-17-0334.1","volume":"31","author":"MC McGraw","year":"2018","unstructured":"McGraw MC, Barnes EA (2018) Memory matters: a case for granger causality in climate variability studies. J Clim 31(8):3289\u20133300. https:\/\/doi.org\/10.1175\/JCLI-D-17-0334.1","journal-title":"J Clim"},{"issue":"2\u20133","key":"9833_CR36","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.pedobi.2015.03.002","volume":"58","author":"N Eisenhauer","year":"2015","unstructured":"Eisenhauer N, Bowker MA, Grace JB et al (2015) From patterns to causal understanding: structural equation modeling (SEM) in soil ecology. Pedobiologia 58(2\u20133):65\u201372. https:\/\/doi.org\/10.1016\/j.pedobi.2015.03.002","journal-title":"Pedobiologia"},{"issue":"12","key":"9833_CR37","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1038\/s41562-018-0466-5","volume":"2","author":"IE Marinescu","year":"2018","unstructured":"Marinescu IE, Lawlor PN, Kording KP (2018) Quasi-experimental causality in neuroscience and behavioural research. Nat Hum Behav 2(12):891\u2013898. https:\/\/doi.org\/10.1038\/s41562-018-0466-5","journal-title":"Nat Hum Behav"},{"issue":"4","key":"9833_CR38","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.97.042207","volume":"97","author":"A Krakovsk\u00e1","year":"2018","unstructured":"Krakovsk\u00e1 A, Jakub\u00edk J, Chvostekov\u00e1 M et al (2018) Comparison of six methods for the detection of causality in a bivariate time series. Phys Rev E 97(4):042207. https:\/\/doi.org\/10.1103\/PhysRevE.97.042207","journal-title":"Phys Rev E"},{"issue":"469","key":"9833_CR39","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1198\/016214504000001880","volume":"100","author":"DB Rubin","year":"2005","unstructured":"Rubin DB (2005) Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc 100(469):322\u2013331. https:\/\/doi.org\/10.1198\/016214504000001880","journal-title":"J Am Stat Assoc"},{"key":"9833_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecosta.2022.01.007","author":"Sh Lee","year":"2022","unstructured":"Lee Sh, Ma Y, de Luna X (2022) Covariate balancing for causal inference on categorical and continuous treatments. Econom Stat. https:\/\/doi.org\/10.1016\/j.ecosta.2022.01.007","journal-title":"Econom Stat"},{"issue":"8","key":"9833_CR41","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1108\/IJM-04-2019-0184","volume":"41","author":"E Rombaut","year":"2020","unstructured":"Rombaut E, Guerry MA (2020) The effectiveness of employee retention through an uplift modeling approach. Int J Manpow 41(8):1199\u20131220. https:\/\/doi.org\/10.1108\/IJM-04-2019-0184","journal-title":"Int J Manpow"},{"issue":"3","key":"9833_CR42","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1214\/ss\/1177010894","volume":"8","author":"J Pearl","year":"1993","unstructured":"Pearl J (1993) [Bayesian analysis in expert systems]: comment: graphical models, causality and intervention. Stat Sci 8(3):266\u2013269. https:\/\/doi.org\/10.1214\/ss\/1177010894","journal-title":"Stat Sci"},{"key":"9833_CR43","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.envint.2016.12.005","volume":"102","author":"LE Brewer","year":"2017","unstructured":"Brewer LE, Wright JM, Rice G et al (2017) Causal inference in cumulative risk assessment: the roles of directed acyclic graphs. Environ Int 102:30\u201341. https:\/\/doi.org\/10.1016\/j.envint.2016.12.005","journal-title":"Environ Int"},{"issue":"6","key":"9833_CR44","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1093\/ije\/dyw341","volume":"45","author":"J Textor","year":"2016","unstructured":"Textor J, Van der Zander B, Gilthorpe MS et al (2016) Robust causal inference using directed acyclic graphs: the R package dagitty. Int J Epidemiol 45(6):1887\u20131894. https:\/\/doi.org\/10.1093\/ije\/dyw341","journal-title":"Int J Epidemiol"},{"issue":"4","key":"9833_CR45","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1097\/EDE.0000000000001031","volume":"30","author":"WD Flanders","year":"2019","unstructured":"Flanders WD, Ye D (2019) Limits for the magnitude of m-bias and certain other types of structural selection bias. Epidemiology 30(4):501\u2013508. https:\/\/doi.org\/10.1097\/EDE.0000000000001031","journal-title":"Epidemiology"},{"issue":"4","key":"9833_CR46","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1097\/EDE.0000000000001032","volume":"30","author":"LH Smith","year":"2019","unstructured":"Smith LH, VanderWeele TJ (2019) Bounding bias due to selection. Epidemiology 30(4):509. https:\/\/doi.org\/10.1097\/EDE.0000000000001032","journal-title":"Epidemiology"},{"issue":"4","key":"9833_CR47","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1111\/rssa.12594","volume":"183","author":"EL Ogburn","year":"2020","unstructured":"Ogburn EL, Shpitser I, Lee Y (2020) Causal inference, social networks and chain graphs. J R Stat Soc Ser A Stat Soc 183(4):1659\u20131676. https:\/\/doi.org\/10.1111\/rssa.12594","journal-title":"J R Stat Soc Ser A Stat Soc"},{"key":"9833_CR48","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/978-1-4899-1424-8_9","volume-title":"Mathematical models for handling partial knowledge in artificial intelligence","author":"J Pearl","year":"1995","unstructured":"Pearl J (1995) From Bayesian networks to causal networks. In: Coletti G, Dubois D, Scozzafava R (eds) Mathematical models for handling partial knowledge in artificial intelligence. Springer, pp 157\u2013182. https:\/\/doi.org\/10.1007\/978-1-4899-1424-8_9"},{"key":"9833_CR49","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl J (2009) Causality. Cambridge University Press, Cambridge. https:\/\/doi.org\/10.1017\/CBO9780511803161"},{"key":"9833_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-27752-1","volume-title":"New introduction to multiple time series analysis","author":"H L\u00fctkepohl","year":"2005","unstructured":"L\u00fctkepohl H (2005) New introduction to multiple time series analysis. Springer Science & Business Media, Cham. https:\/\/doi.org\/10.1007\/978-3-540-27752-1"},{"key":"9833_CR51","doi-asserted-by":"publisher","unstructured":"Brito C, Pearl J (2012) Graphical condition for identification in recursive SEM. arXiv preprint arXiv:1206.6821https:\/\/doi.org\/10.48550\/arXiv.1206.6821","DOI":"10.48550\/arXiv.1206.6821"},{"key":"9833_CR52","doi-asserted-by":"publisher","DOI":"10.1002\/9781118619179","volume-title":"Structural equations with latent variables","author":"KA Bollen","year":"1989","unstructured":"Bollen KA (1989) Structural equations with latent variables, vol 210. John Wiley & Sons, Hoboken. https:\/\/doi.org\/10.1002\/9781118619179"},{"key":"9833_CR53","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316436","volume-title":"Linear statistical inference and its applications","author":"CR Rao","year":"1973","unstructured":"Rao CR, Rao CR, Statistiker M et al (1973) Linear statistical inference and its applications, vol 2. Wiley, New York. https:\/\/doi.org\/10.1002\/9780470316436"},{"issue":"3","key":"9833_CR54","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1007\/s11336-021-09811-z","volume":"87","author":"C Gische","year":"2022","unstructured":"Gische C, Voelkle MC (2022) Beyond the mean: a flexible framework for studying causal effects using linear models. psychometrika 87(3):868\u2013901. https:\/\/doi.org\/10.1007\/s11336-021-09811-z","journal-title":"psychometrika"},{"key":"9833_CR55","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.chaos.2018.02.029","volume":"109","author":"G Gajardo","year":"2018","unstructured":"Gajardo G, Kristjanpoller WD, Minutolo M (2018) Does bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and DJIA as the Euro, Great British Pound and Yen? Chaos Solitons Fractals 109:195\u2013205. https:\/\/doi.org\/10.1016\/j.chaos.2018.02.029","journal-title":"Chaos Solitons Fractals"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09833-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09833-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09833-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T10:09:49Z","timestamp":1724148589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09833-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":55,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["9833"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09833-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"30 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The study does not involve Human Participants. The study does not involve Animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors agreed with the content and explicitly consented to submit the manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}