20. Observational studies with all confounders assumed to be measured

20.1 The challenge of causal inference

20.2 Using regression to estimate a causal effect from observational data

20.3 Assumption of ignorable treatment assignment in an observational study

20.4 Imbalance and lack of complete overlap

20.5 Example: evaluating a child care program

20.6 Subclassification and average treatment effects

20.7 Propensity score matching for the child care example

20.8 Restructuring to create balanced treatment and control groups

20.9 Additional considerations with observational studies

20.10 Bibliographic note

20.11 Exercises