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Causal Inference (Biostatistics 140.665)
Instructor
Meeting time Tuesdays, Thursdays 15:00-16:20 pm, W4007
Email: cfrangak@jhsph.edu,
About this course:
An important task in public health and medicine is to evaluate and compare
treatments, programs, and therapies. To make accurate evaluations, it is
important to study (and respect) data on people, that is, which treatments we take and what outcomes we eventually have. For
practical and ethical reasons, studies with people go beyond the experimental
control found in fully laboratory settings, so people who take one treatment
can generally be different prognostically from those
who take another treatment. Causal inference means the framework for defining
what we care about, for designing and analyzing studies, to take data we can
observe between different treatment groups and correctly attribute them
to effects of treatments. The course presents recent developments in
designs and methods to better evaluate treatment effects.
The instructor acknowledges the sharing of ideas and material with Donald Rubin and Guido Imbens
Summaries of the lectures will be posted after each class
Lecture notes
Chapter 1. Introduction and framework
Chapter 2. Completely randomized assignment
Chapter 3. Treatment assignment with known and varying probabilities
Chapter 4. Ignorable treatment assignment and propensity scores Supplement on likelihood
Chapter 5. Studies with multiple treatments -- sequential ignorable assignment
Chapter 6. Studies with nonignorable noncompliance: instrumental variables
Chapter 7.
Studies with multiple partially controlled factors
Problem sets