Between 2013 and 2015, I worked with Jim Speckart and the Social Science Research Institute (SSRI) at Duke to create a series of videos on causal inference. These are nontechnical explanations of the basic methods social scientists use to learn about causality. They're aimed at high school seniors or 1st year undergraduates, and are quite short---around 2 to 5 minutes on average.

Click on any topic below to expand and view the video. Here I give my recommended viewing order, but you can also pick and choose whatever sounds interesting. These are free for everyone to use, so please check them out!

1. Introduction to causality

1. Introduction

2. Measurement

3. Describing data

4. Correlation versus causation

5. Average treatment effects

6. Unit level treatment effects

7. Conditional average treatment effects

8. Counterfactuals

9. Confounders

10. Statistical versus causal inference

11. How to read empirical papers

2. Experiments

1. Controlled experiments

2. Randomized experiments

3. Design of the Oregon Health Experiment

4. Noncompliance in experiments

5. Reading ATEs and CIs: Depression in the Oregon Health Experiment

6. Cholesterol in the OHE

7. Survey nonresponse

8. Survey nonsponse in OHE

9. Introduction to Perry Preschool

10. Perry Preschool: Educational Attainment

11. Perry Preschool: Effects on crime

12. Perry Preschool: Lifetome cost of crime

13. Multiple testing and sample size in Perry Preschool

14. The point of statistical inference

15. Randomized controlled trials

16. Important experimental issues we've ignored

17. Common issues in experiments

18. Difficulties in implementing experiments

19. Lifetime outcomes of treatments

20. Spillovers in experiments

21. Two kinds of natural experiments

22. Benefits of natural experiments

23. Finding data from natural experiments

24. Justifying as-if randomization

25. Analyzing natural experiments

26. Analyzing natural experiments: Effect of property rights on child health

27. Effect of property rights on teenage pregnancy

28. London Cholera outbreak: Introduction

29. London Cholera outbreak: A natural experiment

30. Bounds analysis for missing data

31. Causal inference issues in lab experiments

3. Regression and causality

1. Introduction to regression

2. Basic elements of a regression table

3. Economic development and property rights

4. Using regression to get causal effects: Unconfoundedness

5. Ordinary least squares (OLS)

6. Defining the average effect of treatment on the treated (ATT)

7. How to compute ATE under unconfoundedness, and what not to do

8. Matching methods

9. The lifetime earnings of veterans and nonveterans

10. The unconfoundedness assumption and lifetime earnings of veterans

11. The effect of military service on lifetime earnings: Results

12. The credibility of the unconfoundedness assumption

4. Instrumental variables

1. The logic of instrumental variables

2. The visual logic of instrumental variables

3. IV in action: Education and wages (graphs)

4. IV in action: Education and wages (tables)

5. Some IV terminology

6. The three IV assumptions

7. Refutability and non-refutability of the IV assumptions

8. Indirect inference

9. Two stage least squares (2SLS)

10. Using IVs to solve noncompliance in experiments

11. Using IVs in the Oregon Healthcare Experiment

12. Weak instruments

13. Property rights and economic development: IV analysis

14. The settler mortality instrument

15. Where do instruments come from?

16. Property rights and market beliefs

17. What are we actually getting with IV?

18. Defining LATE: The local average treatment effect

19. The no defiers assumption

20. Computing LATE, part 1

21. Computing LATE, part 2

22. Computing LATE, part 3

23. The pros and cons of LATE

24. ATEs, CATEs, and LATes: What's the difference?

5. Panel data

1. Does the death penalty reduce homicides?

2. The common trends assumption

3. The effect of immigration on employment

4. Graphical analysis of common trends

5. The effect of the minimum wage on employment

6. The effect of the minimum wage on employment: Main results

7. The effect of the minimum wage on employment: Additional results

8. The effect of the minimum wage on employment: Even more results

9. Panel data terminology

10. Three kinds of panel data

11. Individual fixed effects and time varying treatments

12. The random assignment of changes

13. Does posting calorie counts lower calorie consumption?

14. Heterogeneosu treatment effects: The switchers

6. Regression discontinuity

1. Introduction to RD

2. Thistlethwaite and Campbell continued

3. Computing treatment effects at the cutoff

4. School quality and house prices

5. School quality and house prices: Results

6. School quality and house prices: Economic interpretation

7. Fuzzy regression discontinuity

8. Fuzzy RDD and Swiss relgion

7. Modeling

1. Basics of modeling behavior

2. Discrete choice analysis

3. Confounders in discrete choice analysis

4. How do people choose healthcare plans?

5. Senior citizens' preferences for health insurance plans

6. Human produced CO2 and climate change: When experiments are impossible

7. The impact of government stimulus

8. Conclusion

1. Recap

2. The power of assumptions

3. Balacing data and assumptions

4. Topics we skipped

5. Which causal inference method is the best?