A Discrimination-Free Premium Under a Causal Framework
Open accessAbstract
We examine the discrimination-free premium in Lindholm et al. within a theoretical causal inference framework, and we consider its societal context to assess when the pricing formula should be used. We consider the insurance pricing problem through the use of directed acyclic graphs. This particular tool allows us to rigorously define an insurance risk factor in a causal framework. We then use this definition in assessing the appropriate application of the discrimination-free premium through three simplified pricing examples, including a health insurance policy and two personal automobile insurance policies with different coverages. From our findings, we suggest criteria for the application of the discrimination-free premium that is dependent on the risk factors and the social context.
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There is little research on insurance applications, particularly on insurance pricing as a regression problem.
“However, there is little research on insurance applications, particularly on insurance pricing as a regression problem; see Lindholm et al. (2022a), Araiza Iturria, Hardy, and Marriott (2022), and Grari et al. (2022) as examples of recent research in this area.”
From Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models (2024) · cites this paper as Araiza Iturria, Hardy, and Marriott (2022)
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- Authors
- Araiza Iturria, C. A., Hardy, M., & Marriott, P.
- Affiliations
- University of Waterloo (CA)
- Venue
- North American Actuarial Journal
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 28
- Issue
- 4
- Pages
- 801-821
- ISSN
- 1092-0277 (ISSN-L)
- OpenAlex
- W4392905885
- Cited by
- 20
- References
- 25
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 2 · 2021: 1 · 2017: 1
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- North American Actuarial Journal (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (rgpin-2018-03754)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- insurance pricing, causal inference, directed acyclic graphs, health insurance, automobile insurance
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