Statistical methods are discussed for inferring causal effects from data from randomized experiments or observational studies.
Examples will come from many disciplines: economics, education, other social sciences, epidemiology, and biomedical science. Specific examples include evaluations of job training programs, educational voucher schemes, medical treatments, smoking, and military service.
The primary textbook
is "Causal Inference for Statistics, Social, and BiomedicalSciences: An Introduction", by Guido W. Imbens and Donald B. Rubin, Cambridge University Press (2015)
Additional journal articles for discussion will also be made
available.
Learning Objectives
Students will develop expertise to assess the credibility of causal claims and the ability to apply the relevant statistical methods for causal analyses.
Prerequisites
Statistical inference
Teaching Methods
Lectures, presentations of case studies.
Type of Assessment
Final score will be based on evaluation of home assignments, 1 mid-term exam, one final project with oral discussion.
Course program
Part
I:
The
Basic
Framework
-
A
Brief
History
of
the
Potential
Outcome
Approach
to
Causal
Inference
-
A
Taxonomy
of
Assignment
Mechanisms
Part
II:
Classical
Randomized
Experiments
-
A
Taxonomy
of
Classical
Randomized
Experiments
-
Fisher’s
Exact
P‐values
for
Completely
Randomized
Experiments
-
Neyman’s
Repeated
Sampling
Approach
to
Completely
Randomized
Experiments
‐
Regression
Methods
for
Completely
Randomized
Experiments
-
Model‐based
Inference
in
Completely
Randomized
Experiments