Introduction to the week

This week we will think about observation vs reality, potential outcomes, and how to think about causal inference.

Readings

1. Course notes (WIP)
2. Causal Inference is Not Just a Statistics Problem by McGowan, Gerke and Barrett
3. Common structures of bias

Laboratory work

• link for DAGs
• link for quartets

Assignment

Resources

References

1. Causal Inference in R by Barrett, McGowan and Gerke
2. Causal Inference: What If (the book)

Optional readings

1. Causal inference in public health
2. The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data
3. A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks
4. Target Trial Emulation: A Framework for Causal Inference From Observational Data