Causal Design for Decision Making in Business


Content, progress and pedagogy of the module

Managers today need to better understand cause and effect in a organisations where data plays an important role in decision-making. While machine learning and AI tools can help with identifying relationships in data, such standard tools often do not detect cause and effect relationships in the data. This creates a shortcoming for managers and strategists where these algorithms may not allow to answer important questions in business analytics and decision making regarding “what is the effect of X on Y?” or “did X cause Y to change?”. Many prominent firms such as Google, Uber, Zalando, McKinsey and Spotify are investing in their causal data science capabilities.

This module will provide an introduction to the topic of causal inference with a focus on machine learning and AI based problems in business. In this module, students will conceptually learn how to apply causal inference for data and evidence driven decision making, at the intersection of data science and management strategy. Students will be exposed to various examples to apply concepts from causal analyses learnt in the module. The module will first introduce students to the world of causal inference, and cover standard tools that are used in empirical research, such as instrumental variables, regression discontinuity designs, difference-in-differences. The module will also include case studies that cover machine learning and AI based problems in business decisions.

As the module will cover these topics conceptually, students do not need a particular background to take this class. However, some concepts such as conditional means, variances, hypothesis testing and regression will be covered at the beginning of the module. In-class lectures feature case studies and examples of causal inference research designs.

Learning objectives


The objective is that the student after the module possesses the necessary knowledge on:

  • correlation and causation and the inherent differences of these concepts.
  • central theoretical concepts behind a range of causal data science tools and algorithms.
  • the theoretical and practical role of causal inference for data-driven business problems in strategic decisions.


The objective is that the student after the module possesses the necessary skills in:

  • applying causal thinking to explore both theoretical and practical business decisions.
  • identifying on an academic basis the potentials and challenges for applying causal thinking in decision making.
  • presenting and discussing both professional and academic challenges within causal data science for different target groups using relevant software.


The objective is that the student after the module possesses the necessary competences in:

  • independently carrying out casual data analysis to solve real world problems related to business decision making.
  • uniting theory and practice within management theory in relation to causal inference in business analytics.
  • applying a problem-based approach to central challenges within management and causal inference in business analytics.

Type of instruction

For information see § 17.



Name of examCausal Design for Decision Making in Business
Type of exam
Written exam
Individual examination.
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleKausal design for beslutningstagning i erhverv
Module codeKAORS20229
Module typeCourse
Duration1 semester
Language of instructionEnglish
Location of the lectureCampus Aalborg
Responsible for the module


Study BoardStudy Board of Economics and Business Administration
DepartmentAalborg University Business School
FacultyFaculty of Social Sciences and Humanities