Two questions
A causal analysis can fail two ways. The first is structural — the model is built on a wrong assumption about how variables relate, and the answer is precisely wrong rather than approximately right. The second is conceptual — a Bayesian network is brought to a problem it was never the right tool for, and the analysis would be better served by something else entirely.
These four pages address both failure modes. The first two cover the structural pitfalls — confounding, mediation, moderation, and the paradox they produce when handled poorly. The other two cover when a Bayesian network is the right answer and when it isn't.
Pages in this cluster
Confounding & Mediation → Three ways variables relate causally. Adjusting for the wrong one destroys the analysis.
Four structural relationships between variables determine which ones to adjust for and which to leave alone. Getting this wrong does not produce an approximate answer — it produces a precisely wrong one. The correct classification is a logical question about the graph, not a statistical question about the data.
Simpson's Paradox → The aggregate says one thing. Every subgroup says the opposite.
Simpson’s Paradox is the clearest possible demonstration that a confounder omitted from an analysis can reverse the sign of an effect — not attenuate it, reverse it. The same data supports opposite conclusions depending on whether you adjust for the right variable.
When to Use a BN → Four conditions distinguish a Bayesian network from every other modeling tool. When all four are present, nothing else fits.
Most modeling tools are predictive. The Bayesian network is the one structure built for the question "what would happen if we changed something?" — and that question is only answerable when the causal graph is on the table.
When Not to Use a BN → A Bayesian network is the right structure for causal reasoning under uncertainty. It is not the right structure for everything.
The case for Bayesian networks is strong. That is exactly why it is worth being precise about where they are the wrong tool — so that the recommendation retains credibility when it matters most.
Why this matters
The recommendation "use a causal model" loses credibility when applied without judgment. The pages here are how that judgment gets made explicit.