TY - JOUR

T1 - Forecasting, interventions and selection

T2 - the benefits of a causal mortality model

AU - Jallbjørn, Snorre

AU - Jarner, Søren F.

AU - Hansen, Niels R.

N1 - Publisher Copyright: © 2023, The Author(s), under exclusive licence to European Actuarial Journal Association.

PY - 2024

Y1 - 2024

N2 - Integrating epidemiological information into mortality models has the potential to improve forecasting accuracy and facilitate the assessment of preventive measures that reduce disease risk. While probabilistic models are often used for mortality forecasting, predicting how a system behaves under external manipulation requires a causal model. In this paper, we utilize the potential outcomes framework to explore how population-level mortality forecasts are affected by interventions, and discuss the assumptions and data needed to operationalize such an analysis. A unique challenge arises in population-level mortality models where common forecasting methods treat risk prevalence as an exogenous process. This approach simplifies the forecasting process but overlooks (part of) the interdependency between risk and death, limiting the model’s ability to capture selection-induced effects. Using techniques from causal mediation theory, we quantify the selection effect typically missing in studies on cause-of-death elimination and when analyzing actions that modify risk prevalence. Specifically, we decompose the total effect of an intervention into a part directly attributable to the intervention and a part due to subsequent selection. We illustrate the effects with U.S. data.

AB - Integrating epidemiological information into mortality models has the potential to improve forecasting accuracy and facilitate the assessment of preventive measures that reduce disease risk. While probabilistic models are often used for mortality forecasting, predicting how a system behaves under external manipulation requires a causal model. In this paper, we utilize the potential outcomes framework to explore how population-level mortality forecasts are affected by interventions, and discuss the assumptions and data needed to operationalize such an analysis. A unique challenge arises in population-level mortality models where common forecasting methods treat risk prevalence as an exogenous process. This approach simplifies the forecasting process but overlooks (part of) the interdependency between risk and death, limiting the model’s ability to capture selection-induced effects. Using techniques from causal mediation theory, we quantify the selection effect typically missing in studies on cause-of-death elimination and when analyzing actions that modify risk prevalence. Specifically, we decompose the total effect of an intervention into a part directly attributable to the intervention and a part due to subsequent selection. We illustrate the effects with U.S. data.

KW - Causality

KW - Cause elimination

KW - Interventions

KW - Mortality modelling

KW - Risk factors

UR - http://www.scopus.com/inward/record.url?scp=85180233474&partnerID=8YFLogxK

U2 - 10.1007/s13385-023-00372-2

DO - 10.1007/s13385-023-00372-2

M3 - Journal article

AN - SCOPUS:85180233474

JO - European Actuarial Journal

JF - European Actuarial Journal

SN - 2190-9733

ER -