Course curriculum

    1. Introduction and background

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    2. Past, Present and Future Story

    1. Empirical workflow

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    2. Allocating scarce time

    3. Look but don't touch!

    4. Missing data

    5. Merge errors part 1

    6. Merge errors part 2

    7. Hierarchical folder structures

    8. Automation

    9. Name things well

    10. Version control and introduction to git

    11. Creating a GitHub repository

    12. Github assignment

    13. Hidden curriculum quiz

    14. Soft skills

    15. Some more resources

    1. Design vs Model based approaches to causality

    2. Correlation vs causality

    3. Potential outcomes

    4. Decomposing the SDO

    5. Selection bias

    6. Heterogenous treatment effect bias

    7. Heterogenous treatment effect bias

    8. Decomposing simple difference in mean outcomes

    9. Perfect best friend

    10. Independence

    11. Stable Unit Treatment Value Assumption (SUTVA)

    12. Thornton (2008), "The Demand for, and the Effect of, Learning HIV Status", American Economic Review

    13. Replicating Thornton (2008) -- Stata

    14. lady tasting tea

    15. Lady tasting tea -- Stata example

    16. Randomization Inference - short description

About this course

  • $297.00
  • 33 lessons
  • 6 hours of video content

Analyze, discover, apply, improve

Inference causal effects using clever designs and non-experimental data to maximize your objectives