Causal Inference
An important task in many disciplines (e.g., public health, medicine, economics)
is to evaluate and compare treatments and programs. 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. |
