Back-door path

From Machinelearning

A back-door path in a causal graph is an indirect path with an arrow into the treatment/causal variable.

Formally, given a graph with nodes including (treatment/causal variable) and (outcome variable), a direct path from to is just a directed edge . An indirect path from to is some traversal of edges from to that isn't a direct path. Importantly, the edges don't have to be pointed in the "right way", so is an indirect path. A back-door path is an indirect path with an arrow into .

"a back-door path is defined as any path between the causal variable and the outcome variable that begins with an arrow that points to the causal variable" (p. 30).[1] So this definition doesn't mention indirect paths, but I guess it's implied, because a direct path from the outcome variable to the causal variable would mean that the causal variable isn't really a causal variable?

Examples

  • is a back-door path from to : it is indirect (involves ) and has an arrow into (from ).
  • is not a back-door path from to : it is an indirect path but there is no arrow into .

References

  1. Stephen L. Morgan; Christopher Winship. Counterfactual and Causal Inference: Methods and Principles for Social Research. 2nd ed. Cambridge University Press. 2015.