"portray a wide array of causes as a causal network"

Em Junho de 2009 no postal "Fazer a mudança acontecer (parte VI e meio)" escrevi:

"Durante muitos anos utilizei o diagrama de causa-efeito para organizar, para arrumar as diferentes causas que podem estar na origem de um dado efeito.
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Continuo a usá-lo para problemas de menor dimensão.
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Contudo, para problemas mais complexos, considero a sua abordagem cada vez mais "perigosa" porque veícula uma visão demasiado linear do mundo."

Em Julho de 2018, leio o artigo "Explaining Explanation, Part 3: The Causal Landscape", publicado por IEEE Computer Society em IEEE Intelligent Systems em Março/Abril de 2018, onde encontro:

"The concept is to portray a wide array of causes as a causal network, to help people escape from their single-cause, determinate mindset, but then to highlight a smaller number of causes that matter the most and that suggest viable courses of action. These are the causes that: (a) contributed most heavily to the effect (if they hadn’t occurred, neither would the effect), and (b) are the easiest to negate or mitigate.
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When we want to take steps to prevent an adverse event, the highlighted nodes in a causal network are the places to start exploring.
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The causal landscape’s two-step method highlights the few causes worth addressing through: their impact score, which reflects how much each cause influenced the effect; and their reversibility score, which reflects the ease of eliminating that cause. The causes that had the strongest impact and are the easiest to reverse are the ones that offer the greatest potential to prevent future accidents or adverse events.
The causal landscape is a hybrid explanatory form that attempts to get the best of both worlds—both triggering and enabling causes. It portrays the complex range and interconnection of causes and identifies a few of the most important ones. Without reducing some of the complexity, we’d be confused about how to act."