Forecasting, interventions and selection: the benefits of a causal mortality model
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Forecasting, interventions and selection : the benefits of a causal mortality model. / Jallbjørn, Snorre; Jarner, Søren F.; Hansen, Niels R.
In: European Actuarial Journal, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 384910886