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How Social Care Services can be designed using cause-effect models and Bayesian analysis. A study in Scotland

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Published: Jan 4, 2026
DOI: https://doi.org/10.12681/healthresj.38338
Keywords:
Bayesian probability cause-effect cohorts self- and cross-causality data frames services prediction
Sotirios Raptis
Scottish Government, National Health Services Scotland (NHSS), UK/Scotland, Abertay University Dundee
https://orcid.org/0009-0000-8828-8173
Abstract

Objective: The paper aims to model Health-and-Social-Care (H&Sc) services as Cause–Effect (CE) groups within a Bayesian framework, using cross- and self-causation dynamics. The contribution is that the data used are open and never studied before and are posted online by National Health Services Scotland (NHSS).


Method and Material: The paper contributes to the ongoing discourse on how Machine Learning(ML), and Bayesian inference, can inform the purposes of policymaking. Cause–effect relationships and Bayesian methods can associate public services as causes-effects, through suitable likelihood functions and priors that binomial and normal distributions can imlplement. The study identified the optimal predictive distribution using the Maximum-a-Posteriori (MAP) estimation method. Moreover, the CE-matrix approach, enables the representation of multiple causes linked to a single effect-target in a tabular format, that facilitates interpretability and prediction.


Results: The findings indicate that services related to 'Alcohol' can be predictors of other effect-services, while home-based services were identified as causes of subsequent hospital admissions. Moreover, low-demand services were observed in earlier years, particularly those with no records after 1997, whereas higher-demand services were newly introduced in later years. These findings may offer insights into latent inter-service relationships, and inform policy development. The cross- and self-causation in a Bayesian framework, determined that the posterior can be predicted by 5 to 10 previous observations and this is significantly affected by the level of zero-padding (percentage of past no-records). In later years, the CE models yield more probable demand patterns. Cause–effect relationships were identified between smoking-related services, mental-health support, and the epidemiological index of Primary-1-Education children's Body-Mass-Index (BMI).


Conclusions: The conclusions drawn from this analysis may be particularly relevant for insurance providers and public policymakers, who can leverage Bayesian CE-linked service models for long-term care planning, especially for elderly and low-income populations. The validation of ser-vice interlinkages further enhances the potential for precise and efficient resource allocation.

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References
Castro, M., Mendes Junior, P.R., Soriano-Vargas, A. et al. Time series causal relationships discovery through feature importance and ensemble models. Sci Rep 13, 11402 (2023). DOI : Available at: https : //doi.org/10.1038/s41598 − 023 − 37929 − w
Simon Bottery, et al. A fork in the road: next steps for social care funding reform . The King‘s Fund . Available at: https : //www.kingsfund.org.uk/publications/fork − road − social − care − funding − reform
Koopmans E, Schiller DC. Understanding Causation in Healthcare: An Introduction to Critical Realism. Qual Health Res. 2022 Jul;32(8-9):1207-1214. DOI: 10.1177/10497323221105737. Epub 2022 Jun 1. PMID: 35649292; PMCID: PMC9350449.Available at: https : //www.ncbi.nlm.nih.gov/pmc/articles/PMC9350449/
Public health Scotland. Data and intelligence (2020). A – Z Subject Index; 2020. Available at: https : //www.isdscotland.org/A − to − Z − index/index.asp.
Shpitser I. Identification in Causal Models With Hidden Variables. J Soc Fr Statistique (2009). 2020 Jul;161(1):91-119. Epub 2020 Jun 30. PMID: 33240555; PMCID: PMC7685307. https : //www.ncbi.nlm.nih.gov/pmc/articles/PMC7685307/
Olier, I., Zhan, Y., Liang, X. et al., Causal inference and observational data, BMC Med Res Methodol 23, 227 (2023) . Available at: https : //bmcmedresmethodol.biomedcentral.com/ arti-cles/10.1186/s12874 − 023 − 02058 − 5 DOI: https://doi.org/10.1186/s12874-023-02058-5
Albert Maydeu-Olivares, Dexin Shi, Amanda J. Fairchild, Estimating causal effects in linear regression models with observational data: The instrumental variables regression model . Psychological methods 1 April 2020 Psychology, Economics . Available at: https : //www.semanticscholar.org/paper/Estimating − causal − effects − in − linear − regression − with − Maydeu − Oli-vares − Shi/b91a8c4401b93f6db1dd52a1e33ad88522fc5747 . DOI:10.1037/met0000226Corpus ID: 195879758
Ganopoulou (2023). Maria Ganopoulou, Dimitrios Ko-paranis , et al. Causal Structure assessment in Health-Related Quality of Life questionnaires‖, Conference: 35th Panhellenic and 1st International Statistics Conference, Athens, Greece, Available at: https : //www.researchgate.net/publication/371169040 . DOI:10.13140/RG.2.2.32272.58881 Salman Afsar,
Hengyi Hu,Larry Kerschberg, Improving Causal Bayesian Networks Using Expertise in Authoritative Medical Ontol-ogies, ACM Transactions on Computing for Healthcare, Volume 4, Issue 4, Article No.: 20pp 1–32, Available at: https : //dl.acm.org/doi/10.1145/3604561 , DOI:https://doi.org/10.1145/3604561
Joachim P. Sturmberg, James A. Marcum, From cause and effect to causes and effects, 3 March 2017 . Available at: https : //onlinelibrary.wiley.com/doi/full/10.1111/jep.13814, DOI:https://doi.org/10.1139/er-2016- 0109
Stefano Cucurachi and Sangwon Suh, Cause-effect analy-sis for sustainable development policy, Environmental Re-views . Available at: https : //cdnsciencepub.com/doi/abs/10.1139/er − 2016 − 0109
European Labour Authority Measuring the effectiveness of policy approaches and performance of enforcement authorities , November 2022, Available at: https : //www.ela.europa.eu/sites/def ault/f iles/2023 − 02/Output − paper − from − plenary − thematic − discus-sion − measuring − the − effectiveness − of − policy − approaches − and − performance − of − enforcement − authorities − %282022%29.pd
BPF. The Impact of Rent Control on the Private Rented Sector ‖, Available at: https : //bpf.org.uk/media/6296/2023 - 03 − the − impact − of − rent − control − on − the − private − rented − sector − bpf − f inal.pdf4 . May 2023
Wenkai Hu, Jiandong Wang, Fan Yang, Banglei Han, Zhen Wang, Analysis of time-varying cause-effect relations based on qualitative trends and change amplitudes, Computers & Chemical Engineering, Volume 162,2022, 107813,ISSN 0098-1354, Available at: https : //www.sciencedirect.com/science/article/abs/pii/S00981354220015DOI: https://doi.org/10.1016/j.compchemeng.2022.107813
KAY H. BRODERSEN, FABIAN GALLUSSER, JIM KOEHLER, NICOLAS REMY AND STEVEN L. SCOTT, ‖INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURAL TIME-SERIES MODELS‖ ,The Annals of Applied Statistics 2015, Vol. 9, No. 1, 247–274. Available at: https : //static.googleusercontent.com/media/research.google.com/en//pubs/archive/41854.pdf . DOI: 10.1214/14- AOAS788.2015
Oki Oktaviani , Budi Susetyo, Bambang Purwoko Kusumo Bintoro, Risk Management Model using Cause and Effect Analysis in Industrial Building Project ‖, International Journal of Research and Review, Vol.8; Issue: 8; August 2021 . Available at: https : //www.ijrrjournal.com/ IJRRV ol.8I ssue.8Aug2021/IJRR032.pdf, DOI: https://doi.org/10.52403/ijrr.20210832 . www.ijrrjournal.com . ISSN: 2349-9788; P-ISSN: 2454-2237
Yang C, Delcher C, Shenkman E et al. Expenditure varia-tions analysis using residuals for identifying high health care utilizers in a state Medicaid program. BMC Med In-form Decis Mak. 2019. Available at: https :; 19(1) : 131
Atul Gupta, Joseph R. Martinez, Amol S. Navathe, SELEC-TION AND CAUSAL EFFECTS IN VOLUNTARY PROGRAMS: BUNDLED PAYMENTS IN MEDICARE, Working Paper 31256, NBER WORKING PAPER SERIES. Available at: nber.org/system/f iles/workingpapers/w31256/w31256.pdf
Chen Chen, Bin Huang, Michal Kouril, et al. An applica-tion programming interface implementing Bayesian ap-proaches for evaluating effect of time-varying treatment with R and Python,Front. Comput. Sci., 16 August 2023, Volume 5 - 2023. Sec. Software, Available at: https : //www.frontiersin.org/articles/10.3389/ fcomp.2023.1183380/full . DOI: https://doi.org/10.3389/fcomp.2023.1183380
Scottish Governement, statistics.gov.scot . Available at: https : //statistics.gov.scot/datahome
Aristotelis Koskinas, Eleni Zaharopoulou, George Pouliasis , Ilias Deligiannis, ‘Estimating the Statistical Significance of Cross–Correlations between Hydroclimatic Processes in the Presence of Long–Range Dependence‘. Available at : https : //www.itia.ntua.gr/el/getf ile/2234/1/documents/earth − 03 − 00059 − v3.pdf ,
Vidushi Adlakha and Eric Kuo Statistical causal inference methods for observational research in PER: a primer . Available at: https : //arxiv.org/pdf /2305.14558.pdf
Md Saiful Islam, Md Mahmudul Hasan (2018) A Systemat-ic Review on Healthcare Analytics: Application and Theo-retical Perspective of Data Mining, Healthcare‘, 2018, pp. 6-54. Available at: 10.3390/healthcare6020054