Congratulations! You’ve completed the Potential Outcomes Framework lesson and now have a solid conceptual foundation for causal inference!
In this lesson, you learned:
- Causation is different from association in that it implies a relationship where a change in one variable leads to a change in another variable.
- If we could view both potential outcomes, we could accurately compute the true effect of a treatment.
- The fundamental problem of causal inference is that we only get to view the observed outcome and not its counterfactual.
- When randomization is not available to produce estimates of treatment effects, we must use other strategies to predict counterfactuals and find our estimand of interest.
- The three main assumptions for causal inference are:
- Conditional exchangeability
- The causal inference process can be thought of as two steps: identification and estimation.