Learn

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
    • SUTVA
    • Overlap
  • The causal inference process can be thought of as two steps: identification and estimation.

Take this course for free

Mini Info Outline Icon
By signing up for Codecademy, you agree to Codecademy's Terms of Service & Privacy Policy.

Or sign up using:

Already have an account?