DS839: Causal Analysis in Business Data Science

Study Board of Science

Teaching language: Danish or English depending on the teacher
EKA: N340132102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340132101
ECTS value: 5

Date of Approval: 30-10-2023


Duration: 1 semester

Version: Approved - active

Entry requirements

None

Academic preconditions

Familiarity with statistical analysis (DS803 and DS810). Knowledge of the software R is recommended but not required.

Course introduction

The main purpose of the course is  to introduce students to causal analysis with a particular focus on its applications to data driven decision making.

The course will provide the student with knowledge about the central methods to identify and estimate causal effect and their intersection with big data analytics.
The students will have the skills to apply these methods to solve relevant empirical problems.
The  course will give the students the competence to inform policy makers and businesses  in relation to a wide range of problems that require to establish casual reletionships.

In relation to the competence profile of the degree it is the explicit focus of the course to:
•Give knowledge about and practical experience with the application of basic and advanced causal and machine learning methods and models
•Give competence to apply learned methods to concrete problems
•Give competence to illuminate hypotheses on a qualified theoretical background
•Give competence to promote collaboration and communication between data science and domain experts

The course builds upon knowledge from the course DS803 Statistics for Data Science as well as DS810 Data Driven Decision Making. The course provides a basis for possible thesis topics within causuality and machine learning. 
 


    Expected learning outcome

      To fulfil the purposes of the course the student must be able to demonstrate knowledge about the course’s focus areas enabling the student to:
      • Describe, explain and apply the different methods/models covered.
      • Describe and explain how the different models are estimated.
      • Describe and explain the difference between correlation and causation.





      Content

        The following topics are covered:
        1.    Introduction to causal inference: potential outcomes and DAGs
        2.    Intersection between causal inference and machine learning
        3.    Advanced causal inference methods

        Literature

        See itslearning for syllabus lists and additional literature references.

        Examination regulations

        Exam element a)

        Timing

        June

        Tests

        Take home assignment

        EKA

        N340132102

        Assessment

        Second examiner: Internal

        Grading

        7-point grading scale

        Identification

        Student Identification Card - Exam number

        Language

        Normally, the same as teaching language

        Duration

        72 hour take home exam

        Examination aids

        All aids allowed

        ECTS value

        5

        Additional information

        The assignement is handed in, in Digital Exam.

        Indicative number of lessons

        21 hours per semester

        Teaching Method

        At the faculty of science, teaching is organized after the three-phase model i.e. intro, training and study phase. These teaching activities are reflected in an estimated allocation of the workload of an average student as follows:

        Intro and training phases: 21 hours (divided approximately evenly into lectures and exercises)

        Activities during the study phase:
        Reading of course material
        Reflection on methods and theoretical concepts
        Solving selected exercises in R 
        Preparing a group presentation
        Preparation for lectures
        Preparation for exam







          Teacher responsible

          Name E-mail Department
          Christian Møller Dahl cmd@sam.sdu.dk Econometrics and Data Science
          Giovanni Mellace giome@sam.sdu.dk Econometrics and Data Science

          Timetable

          Administrative Unit

          Institut for Matematik og Datalogi (datalogi)

          Team at Educational Law & Registration

          NAT

          Offered in

          Odense

          Recommended course of study

          Profile Education Semester Offer period

          Transition rules

          Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
          If a transitional arrangement has been made for a course, it will be stated in the list. 
          See transitional arrangements for all courses at the Faculty of Science.