Why the Markov blanket matters
A node's Markov blanket is its information boundary: the minimal set of variables that makes it conditionally independent of everything else in the network. Once you know the blanket, no further observation — however strongly correlated — can improve your prediction of the target node.
For prediction, the blanket defines the minimum sufficient feature set; adding variables outside it yields no lift regardless of sample size. For intervention design, it distinguishes direct levers (parents) from downstream effects (children) from indirect confounders (co-parents). For auditing, it reveals which variables must be controlled to isolate a causal effect. The blanket is also where fairness and privacy questions land: only variables inside it can causally influence the outcome.
- Open the recommended scenario for this case
- Adjust observed evidence or intervention settings
- Move to a second tool without losing context
- Compare obs() versus do() where available
- Inspect paths, blankets, or CPT structure to explain the shift