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A Comparison Between Structural Equations and Predictive Modelling Approaches for Estimation of Causal Effects for Hospital Outlier on Patient Outcomes: The Case of Patient Care Units in Dalarna Region, Sweden
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Many studies have been done on the identification of the causal effect of a treatment (or intervention) but still the field of causal effect identification is highly debated in many disciplines e.g. data science, econometrics, biostatistics etc. This study examined the theoretical motivation of predictive modelling approach to estimate and check the sensitivity analysis for causal effects by using structural equations modelling framework with application of real data from Landstinget Dalarna. In this study, the link between causal inference using structural equations modelling approach of Heckman's two-step estimation framework and predictive modeling approach has been established. Furthermore, two different simulation studies under linearity and nonlinearity assumptions were conducted to see the finite sample properties of the predictive modelling approaches such as Support Vector Machine, Random Forest, Gaussian boosted regression trees and Bayesian additive regression trees. Finally, the predictive modelling approaches were used to estimate the treatment effects on patient outcomes in terms of length of stay, unplanned readmission and mortality within 30 days after discharge. The results were also compared with the traditional approaches: propensity score matching, propensity weighting and two-step estimation method. This study used two types of estimates, average treatment effects and treatment on treated. The estimates and their standard errors were calculated for the real data from Landstinget Dalarna. The study found that, non-outlying patients are staying in the hospital for longer periods of time (in days) compared to outlying patients though the reasons that make long of stay remained unknown. For readmission and mortality, the results varied a lot between the alternative models, and we can therefore not conclude that non-outlying patients have higher mortality and readmission rates compared to outlying.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Heckman two step estimation (HM), Predictive modelling, Outlying patients and Non-outlying patients.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:du-30808OAI: oai:DiVA.org:du-30808DiVA, id: diva2:1355222
Available from: 2019-09-27 Created: 2019-09-27

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf