do-operator
A core symbolic tool in causal inference proposed by statistician Judea Pearl, used to distinguish the observational conditional probability P(Y|X=x) from the interventional probability P(Y|do(X=x)). It formalizes active interventions by performing graph surgery to sever a variable's upstream causes.
Timeline (last 90 days)
Causal inference uses the do-operator to express active intervention; do(administering medication to a patient) and observing that a patient is on medication are different operations.
When a do-operation forcibly changes a variable, it is equivalent to severing that variable's original incoming paths, causing it to no longer carry its original information.
P(Y | do(X=x)) is equivalent to simulating a randomized controlled trial.
All Facts (3)
Causal inference uses the do-operator to express active intervention; do(administering medication to a patient) and observing that a patient is on medication are different operations.
50%UnverifiedWhen a do-operation forcibly changes a variable, it is equivalent to severing that variable's original incoming paths, causing it to no longer carry its original information.
50%UnverifiedP(Y | do(X=x)) is equivalent to simulating a randomized controlled trial.
50%