Information for students, faculty and staff regarding COVID-19. (Updated: 27 November 2020)

Syllabus:

# Causal inference, 7.5 Credits

Swedish name: Kausal inferens

This syllabus is valid: 2018-12-31 and until further notice

Course code: 2ST054

Credit points: 7.5

Education level: Second cycle

Main Field of Study and progress level: Statistics: Second cycle, has only first-cycle course/s as entry requirements

Grading scale: VG Pass with distinction, G Pass, U Fail

Responsible department: Department of Statistics

Established by: Rector of Umeå School of Business and Economics, 2018-09-27

## Contents

Causal inference is the goal of many empirical studies in the health and social sciences. In a causal analysis, a causal parameter is formally defined and the underlying assumptions are explicitly stated. These assumptions clarifies the differences between experimental data and observational data for making causal conclusions. In the course we focus on two dominating approaches for causal analyses, i) the potential outcomes framework and ii) structural analyses (graphical models). We learn the foundations for these two frameworks as well as methods for estimation of causal effects in experimental and observational studies. Data-examples are given throughout the course and practical problem solving and data analyses are carried out in parallel with the theoretical material.

## Expected learning outcomes

Knowledge and understanding
After a completed course the student should be able to show:
• an in-depth knowledge of the potential outcomes framework and use of directed acyclic graphs for causal inference;
• an in-depth understanding of assumptions underlying causal analyses with experimental and observational data;
• an in-depth knowledge of the presented methods to draw causal inference.
Skills and abilities
After a completed course the student should be able to:
• apply parametric and non/semi-parametric estimators of causal effects and understand the corresponding underlying assumptions;
• perform causal analyses with statistical software.
Ability to evaluate and approach
After a completed course the student should be able to:
• evaluate the plausibility of underlying assumptions in experimental and observational studies;
• evaluate the sensitivity of a causal effect estimate.

## Required Knowledge

Univ: 90 credits (hp) in statistics and/or mathematical statistics, or equivalent. Proficiency in English equivalent to Swedish upper secondary course English B (English/6)

## Form of instruction

The course consists of lectures and lessons. There are mandatory assignments where the students shall present their solutions.

## Examination modes

The examination partly consists of individual written reports and oral presentations of given assignments. Written reports of assignments should be handed in or presented orally at predetermined dates. The grades for the assignments are: G (Pass), and U (Fail).

The examination also includes a written exam. The grades for the written exam are: G (Pass), U (Fail), VG (Pass with distinction).

Grades on the course are awarded when students have passed all examinations in the course. The grade is a comprehensive evaluation of the results of the various parts of the examinations and is not granted until all mandatory tasks have been passed.

A student who has passed an examination is not allowed to take another examination in order to get a higher grade. For students who do not pass, an additional test will be held according to a pre-determined schedule.

After two failed examinations on the course, the student has the right to request another grading teacher. Written requests should be handed to the Director of Studies no later than two weeks before the date of the next examination.

Examinations based on the same course syllabus as the ordinary examinations are guaranteed to be offered up to two years after the date of the student's first registration for the course.

Academic credit transfers are according to the University credit transfer regulations.

## Literature

### Valid from: 2019 week 1

Recent developments in the econometrics of program evaluation
Imbens G.W., Wooldridge J.M.
Included in:
Journal of economic literature.
Nashville, Tenn. The Assoc. : 1969- : 47 : pages 5-86 :
Mandatory

Data analysis using regression and multilevel/hierarchical models
Gelman Andrew., Hill Jennifer
2007 : 1 online resource (xxii, 625 pages) :
http://dx.doi.org/10.1017/CBO9780511790942
ISBN: 9780511769559
Mandatory
Search the University Library catalogue
Reading instructions: Chapter 9 and 10

Estimating causal effects for multivalued treatments: a comparison of approaches
Linden A., Uysal S.D., Ryan A., Adams J.L.
Included in:
Statistics in medicine.
Chichester : Wiley : 1982- : 35 : pages 534-552 :
Mandatory

Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Lunceford J.K., Davidian M.
Included in:
Statistics in medicine.
Chichester : Wiley : 1982- : 23 : pages 2937-2960 :
Mandatory

Causal inference in statistics : a primer
Pearl Judea, Glymour Madelyn, Jewell Nicholas P.
2016 : 136 s. :
ISBN: 9781119186847
Mandatory
Search the University Library catalogue

Rosenbaum P.R.
Sensitivity analysis in observational studies
Included in:
Encyclopedia of statistics in behavioral science
Chichester : John Wiley : cop. 2005 : 4 vol. (2208 s.) : pages 1809-1814 :
Mandatory

Further papers and book chapters may be included, around 50 pages.