Results pathways are usually based on a theory of change describing how change is assumed to come about through intervention/s in a prevailing situation. The theory is laid out in a diagram showing the connections between interventions and outcomes  the results pathways. From an epidemiological perspective, these results pathways can be conceived as directed acyclic graphs (DAG) more specifically those concerned with structural causal models that portray causal assumptions about a set of variables. Causal DAGs are consistent and easy to understand. Thus, when assessing the causal effect between an intervention and an outcome, drawing assumptions in the form of a DAG can help in picking the right model without necessarily having the mathematical basis behind it. Another way to think about DAGs is as nonparametric structural equation models (SEM) which explicitly lays out paths between variables. In the case of a DAG, the exact relationship between the two variables is not as important, only its direction. The rules underpinning DAGs are consistent whether the relationship is da simple, linear one, or a more complicated function.
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