Background <p>Long COVID is an infection-associated chronic condition with uncertain evolution, leading to ambiguity in case definitions and various hypotheses about its pathophysiology. Despite this diversity, causal models may offer a unified understanding of post-acute COVID-19 mechanisms. This study aimed to examine whether dynamic Bayesian networks could facilitate inferences on long COVID.</p> Methods <p>Using a causal engineering approach, we developed directed acyclic graphs and qualitatively parametrised them as Bayesian networks to depict the hypothesised mechanisms of long COVID in a theory-agnostic manner. Based on the literature and expert knowledge, we created a general modelling framework summarising biological pathways from mild or severe COVID-19 to the development of respiratory symptoms and fatigue over four key periods (<i>t</i>₁ to <i>t</i>₄). We used qualitative parametrisation for design and validation, and tested the framework against four scenarios: A) mild COVID-19 at <i>t</i>₁ (start of acute infection); B) severe acute COVID-19 at <i>t</i>₁; C) symptoms reported at <i>t</i>₁ (acute COVID-19 disease); and D) symptoms reported at <i>t</i>₁ and <i>t</i>₃ (e.g., 3-to-6 months post-acute infection), indicating long COVID.</p> Results <p>Here we show that, in scenario A, the probability of progressing to severe disease and developing persistent organ dysfunction 1-to-2 years post-acute COVID-19 was lower than in scenario C. Those reporting symptoms at <i>t</i>₁ and <i>t</i>₃ have the highest probability of developing persistent organ dysfunction beyond the acute infection period.</p> Conclusions <p>Our findings lay the foundations for a better understanding of the progression of long COVID syndromes. Illustrative simulations support the use of causal models to help address both diagnostic and prognostic questions in long COVID research.</p>