任课教师 Speaker：Per Johansson
时间 Time： 每周二&四15:20-16:55 2021-2-22 ~ 5-14
地点 Venue：线上
This course
focuses on the identification and estimation of causal effects. The course is
divided into three blocks.
The first block
introduces the concepts of potential outcomes, estimands and discusses
inference of casual effect in a randomized controlled trial (RCT).
The second block
discusses causal inference with observational data, that is data that are
collected from registers or surveys. The focus is on, what is known as,
quasi-experimental designs. The reason for the definition is that the modelling
builds on the concepts from the RCT. We first discuss the Rubin Casual Model.
The fundamental assumption for this model is the unconfoundedness assumption,
which means that we can create an RCT using pre-treatment observed covariates.
We then discusses designs and/or methods that do not rely on the
unconfoundedness assumptions. Methods or design often used in micro-econometric
applications.
The
third block discuss models that add more structure to the estimation but
consider also partial identification and bounds.
An
understanding of regression analysis
Imbens,
G. W. and Rubin, D. B. (2015). Causal Inference in Statistics, Social, and
Biomedical Sciences. Causal Inference for Statistics, Social, and Biomedical
Sciences: An Introduction. Cambridge University Press
Imbens
G.W. and J.M. Wooldridge (2009). Recent Developments in the Econometrics of
Program Evaluation. Journal of Economic Literature 47,. 5-86
Wooldridge J. (2010).
Econometric Analysis of Cross section and panel data MIT press.
Zoom Meeting ID：849 963 1368
Password：YMSC
