Statistics, Register Data Analysis and Causal Inference, Second Cycle, 7.5 credits

Aims and objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall

• further develop the ability of students to integrate and make autonomous use of their knowledge
• develop the students' ability to deal with complex phenomena, issues and situations, and
• develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course objectives

Knowledge and Understanding

After completed studies, the student shall have

• knowledge of the qualitative differences in drawing inference on causal relationships when using randomized experiments and observational data, respectively.
• knowledge of basic statistical methods to study causal relationships based on observational data.

Competence and Skills

After completed studies, the student shall be able to

• conduct analyses of register data for the study of causal relationships.

Judgement and Approach

After completed studies, the student shall be able to

• critically evaluate the quality of results from register based analyses.

Main content of the course

The course begins with a discussion on the measurement of treatment effects based on randomized experiments, as well as limitations in the use of observational data for the analysis of causal relationships. Specifically, effects of individuals' selection into the treatment group and the control group are studied, as well as opportunities to control for self-selection.

Different methods for analysis when the selection problem can be controlled via the control of observable variables are thereafter studied. Special focus is given to different matching methods. In addition, techniques for analyzing the sensitivity of results to effects of unobservable factors are introduced.

A final part examines approaches for design and analysis of studies based on observational data when selection effects cannot be controlled with known background variables.

Teaching methods

Teaching consists of a number of lectures and a number of workshops. The lectures aim to outline the areas and problems discussed whereby subsequent workshops are aimed at a student-driven discussion of the specific issues covered in the course.

The course includes a lab on analysis of register data. The work is reported orally an in writing.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Examination methods

Lab, Analysis of Register Data in Practice, 2.5 credits (Code: A001)
Written and oral presentation

Written Examination, 5 credits (Code: A002)
Individual written examination

For students with a documented disability, the university may approve applications for adapted or other forms of examinations.

For further information, see the university's local examination regulations (in Swedish).

Grades

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

In accordance with university regulations regarding grading systems for first and second-cycle courses (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades shall be used: Fail (U), Pass (G) or Pass with Distinction (VG). For courses that are included in an international Master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the grading scale of A-F shall be used. The vice-chancellor, or a person appointed by the vice-chancellor, may decide on exceptions from this provision for a specific course, if there are special grounds.

Grades used on course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Lab, Analysis of Register Data in Practice
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Written Examination
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

To obtain a passing grade for the course as a whole, a passing grade is required on all course components. The final grade for the entire course is a function of the grades of the course components. Detailed information on the requirements for different grade levels is given at the course start.

Specific entry requirements

First-cycle courses of 90 credits in statistics, including an independent project of 15 credits alternatively 30 credits are for studies in statistics and 60 credits for mathematicsas well as the course Statistics, Econometrics, Second Cycle, 7.5 credits. The applicant must also have qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Reading list and other teaching materials

Required Reading

Lee, M.J. (2005)
Micro-Econometrics for Policy, Program and Treatment Effects
Oxford University Press, Oxford