Causal Effects via the Do-operator

Translating observations into interventions

This is the 3rd article in a series on causal effects. In the last post, we reviewed a set of practical approaches for estimating effects via Propensity Scores. The downside of these approaches, however, is they do not account for unmeasured confounders. By the end of this article, we will see how we can overcome this shortcoming. But first, we need to take a step back and reevaluate how we think about causal effects.