ITA | ENG
B026846 - CAUSAL INFERENCE AND PROGRAM EVALUATION
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2022-23
Coorte 2021 - Second Cycle Degree in STATISTICS AND DATA SCIENCE
Course year
Second year - Second Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
SECS-S/01 - STATISTICS
Credits
9
Teaching Hours
72
Teaching Term
20/02/2023 ⇒ 01/06/2023
Attendance required
No
Type of Evaluation
Final Grade
Course Content
show
Course program
show
Lectureship
Mutuality
Course teached as:
B031300 - CAUSAL INFERENCE
Second Cycle Degree in ARTIFICIAL INTELLIGENCE
B031300 - CAUSAL INFERENCE
Second Cycle Degree in ARTIFICIAL INTELLIGENCE
Teaching Language
English
Course Content
Statistical methods are discussed for inferring causal effects from data from randomized experiments or observational studies also in high-semensional settings.
Examples will come from many disciplines: engineering, economics, education, other social sciences, epidemiology, and biomedical science.
Examples will come from many disciplines: engineering, economics, education, other social sciences, epidemiology, and biomedical science.
Suggested readings (Search our library's catalogue)
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.
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 extracting causal information from observed data.
Prerequisites
Probability, statistical inference, regression models
Teaching Methods
Lectures, presentations of case studies, labs.
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
TBD