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📋 '''Structural causal model (SCM)''' is a formal mathematical framework — rooted in directed acyclic graphs and structural equations — that insurance analysts and [[Definition:Actuarial science | actuaries]] use to represent and reason about cause-and-effect relationships among risk factors, [[Definition:Loss | losses]], and [[Definition:Exposure | exposures]]. Unlike purely statistical models that capture correlations, an SCM explicitly encodes which variables influence which, enabling practitioners to distinguish genuine causal drivers of [[Definition:Claims | claims]] from spurious associations. In insurance, this distinction is critical: pricing a [[Definition:Commercial insurance | commercial]] policy or adjudicating a [[Definition:Liability insurance | liability]] claim often hinges on whether a particular factor actually caused an outcome rather than merely co-occurred with it.

⚙️ An SCM comprises a set of variables (such as weather severity, building construction type, policyholder behavior, and claim amount), a directed graph showing how these variables causally relate to one another, and structural equations that quantify each relationship. To apply the framework, an insurance data scientist specifies the causal graph based on domain expertise — for example, encoding that a [[Definition:Catastrophe | catastrophe]] event causes property damage, which in turn drives [[Definition:Claims frequency | claims frequency]] — and then uses techniques like do-calculus to estimate what would happen under hypothetical interventions. This allows [[Definition:Predictive model | predictive models]] to answer counterfactual questions: "What would this portfolio's [[Definition:Loss ratio | loss ratio]] have been if we had excluded a particular peril?" SCMs are also valuable for [[Definition:Fraud detection | fraud detection]], where analysts need to determine whether suspicious patterns genuinely indicate fraudulent behavior or simply reflect confounding characteristics of certain policyholder segments.

🔍 The growing adoption of SCMs across the insurance industry reflects a broader shift from black-box [[Definition:Machine learning | machine learning]] toward explainable and auditable analytics. Regulators in the European Union, the United Kingdom, and parts of Asia increasingly expect insurers to demonstrate that [[Definition:Rating factor | rating factors]] used in pricing are causally relevant — not merely statistically predictive — particularly in lines where [[Definition:Unfair discrimination | unfair discrimination]] concerns arise, such as [[Definition:Motor insurance | motor]] and [[Definition:Health insurance | health]] insurance. SCMs provide the theoretical scaffolding to meet these requirements. For [[Definition:Reinsurance | reinsurers]] modeling [[Definition:Aggregate exposure | aggregate exposures]] across correlated perils, the framework clarifies which dependencies are structural and which are artifacts of historical data, leading to more robust [[Definition:Capital modeling | capital models]] and better-informed [[Definition:Risk transfer | risk transfer]] decisions.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Causal inference]]
* [[Definition:Predictive model]]
* [[Definition:Machine learning]]
* [[Definition:Actuarial science]]
* [[Definition:Rating factor]]
* [[Definition:Targeted maximum likelihood estimation (TMLE)]]
{{Div col end}}

Definition:Structural causal model (SCM) - Revision history

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