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?