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<section begin=glossary />
Causality means that there is a causal influence between two variables (or objects): if the value of the one upstream is changed, the object downstream changes as well. Causal relationships are represented as arrows in causal diagrams. However, the lack of a causal relationship, i.e. a lack of an arrow between two variables is a stronger statement than an arrow. One operationalization of causality is a Bayesian belief network. Causality is also called dependency.
A risk assessment method based on the full-chain approach utilises causality as a major concept. This implies that the assessment products produced in the assessments should be causal network descriptions that cover the relevant phenomena from emissions to exposures to health effects and their impacts in accordance with the chosen endpoints and purpose. However, it should be emphasized that the method does not only describe issues that are associated with the full chain. It describes those issues that cause effects along the full chain, and it describes how the causes and effects are related. This, of course, makes risk assessment a challenging, or even difficult, process. Strict emphasis on causality, however, should be the way to e.g. estimate the impacts of policies on emissions and consequently to health effects. For further details, see Guidance and methods for indicator selection and specification.<section end=glossary />