A very brief practical primer using R and Stan.
Topics (to be) covered:
- Potential outcomes
- Causal diagrams (DAGs)
- Randomization vs. observational settings
- Multiple regression and backdoor paths
- 'Table 2 fallacy', frontdoor criterion, etc.
- Conditional vs. marginal effects
- G-methods and longitudinal analysis
- Missing data and other threats to identification
- Poststratification, generalizability and transportability
- Econometric techniques
- .. and more!