Tel: +49 221 47694-160
Tel: +49 221 47694-160
Week 3: Potential Outcomes and Treatment Effects: Modern Methods of Causal Inference
Prof. Dr. Ben Jann, Dr. Rudolf Farys
Date: 20.03 - 24.03.2017 ics-file
Based on the potential outcomes notation of causal effects (a.k.a. the Rubin Causal Model) a variety of methods for causal inference from observational data have been developed or re-discovered over the last two decades and became increasingly popular in cutting-edge social science research. This course provides an introduction to these methods, explains their foundations and assumptions and discusses the conditions under which their use is appropriate. Topics covered are conceptual approaches such as the potential outcomes framework and directed acyclic graphs (DAG) as well as estimation methods such as matching, inverse probability weighting, instrumental variables, regression discontinuity, and difference-in-difference. Upon completion of this course, students should have acquired skill in the estimation, specification and diagnostics of the various methods and gained hands-on experience with those methods through the use of appropriate software and actual data sets. For the exercises in the computer lab, the course relies on Stata.
Causal Inference, Potential Outcomes, Rubin Causal Model, Conditional Independence, Strong Ignorability, Matching, Regression Adjustment, Inverse Probability Weighting, Instrumental Variables, Regression Discontinuity Design, Difference-in-Difference Design, Directed Acyclic Graphs
Participants will find the course useful if they
- are social science researchers who want to learn about methods of causal inference with observational data.
By the end of the course participants will
- be familiar with the potential outcomes approach to causal inference,
- have an overview of a broad set of methods for causal inference with observational data,
- be familiar with advantages, disadvantages, assumptions and possible applications of the different methods,have practical experience in the application of the methods and the interpretation of the results.
- Sound knowledge of applied statistics and regression modelling.
- Basic knowledge of Stata.
Organisational Structure of the Course
The course consists of classroom instruction in the morning and hands-on exercises in the afternoon.
Software and hardware requirements
None. Course participants will not need to bring a laptop computer for this course. The course will take place in a computer lab.