Cause-effect pair methods might extend the potential outcome framework to resolve cases with no a priori on causal direction X → Y or X ← Y . All is needed is to first condition P ( X , Y ) on known potential confounders Z (e.g. age, gender, etc.) or adjust with a propensity score to get P ( X , Y | Z ), then address edge orientation as a cause-effect pair problem.

The setting of cause-effect pairs, making no a priori assumption on causal orientation nor on the presence of hidden confounders, lends itself to applications in areas like epidemiology, when it is unclear which variable is the “treatment” and which one is the “outcome”, e.g. X = diabetes_consumption and Y = drinking_diet _soda .