Application of Bayesian Methods to Causal Inference and Decision Making in Health Care and Public Policy in Uganda
DOI:
https://doi.org/10.47604/jsar.2310Keywords:
Bayesian Methods, Causal Inference, Decision Making, Health Care, Public PolicyAbstract
Purpose: The aim of the study was to investigate application of Bayesian methods to causal inference and decision making in health care and public policy
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: The use of Bayesian methods in healthcare and public policy has proven effective for estimating causal effects and making informed decisions. By integrating prior knowledge and data, Bayesian approaches enhance transparency and credibility in policy recommendations, leading to more evidence-based and effective interventions in these sectors.
Unique Contribution to Theory, Practice and Policy: Bayesian decision theory, Causal inference theory & Health economics and Bayesian analysis may be used to anchor future studies on application of Bayesian methods to causal inference and decision making in health care and public policy. Implement Bayesian methods to tailor health care interventions and treatment plans to individual patients. Encourage the integration of Bayesian methods into decision support systems for healthcare and public policy
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