Looking Ahead

🌱 Summary

In this lesson, you learned:

  • What causal inference is — the science of figuring out what causes what and separating true effects from confounding relationships.
  • What a randomized experiment is — a study design where participants are randomly assigned to treatment and control groups, allowing us to make causal claims.
  • How to estimate a treatment effect — by comparing the average outcomes of the two groups.
  • That not all data comes from experiments — sometimes we rely on observational studies, where we observe what happens naturally rather than assigning treatments ourselves.

🔍 Key Takeaways

  • Randomization helps ensure that differences between groups are due to the treatment, not preexisting factors.
  • The difference in group averages gives us a first look at the treatment effect — but future lessons will help us decide whether the difference is statistically significant.
  • Experiments aren’t always possible — sometimes for ethical, practical, or cost reasons — and that’s where observational data becomes essential.

🚀 What’s Next

In the next section, we’ll move beyond simple comparisons and start exploring:

  • How the design of the experiment can be improved.
  • How to handle data from observational studies.
  • How to begin thinking about confounders and adjustment.