For students:

Causal Inference and Data Analytics (5 cr)

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Code:
26030
Field:
Econometrics
Target:
Bachelor's students
Organiser:
Hanken Shool of Economics
Instructor:
Ari Hyytinen
Period:
Period 3
Format:
Lecture
Method:
Contact teaching
Venue:
Hanken
Enrollment:
How to enroll?

In case of conflicting information consider the Sisu/Moodle pages the primary source of information.

Aalto and UH economics students can enroll in their home university’s SISU! Further instructions can be found on the How to enroll? page, also for other students.

Before taking and completing the course make sure that the credits can be counted towards your degree at your home university by checking which courses are included in your curriculum or by contacting your home university’s student/learning services.

  • A Moodle course key will be sent by email (to your Hanken email address) or it is posted as a message in Sisu couple of days before the course starts.
  • Log in with your Hanken username to be able to use all the features of the course workspace.
  • More tips for enrolling in Moodle can be found here.

Content

This course introduces students with the basics ofcausal inference and data analytics, with special emphasis on modern applied micro-econometric methods. The aim of the course is to help students to build and develop skills needed to understand empirical methods that are used in modern causal inference and to execute their own econometric projects. The course focuses on the following topics and methods: randomized trials, regression, instrumental variables, regression-discontinuity designs, and differences-in-differences. We also explore a number of applications, mainly in economics and finance. The course also introduces students with econometric and statistical software and how it can be used in causal inference and data analysis.

Learning outcomes

After completing the course, you will be able to:

  • identify and explain key concepts and methods used in causal inference
  • explain what causality means and why and when causal inference methods work
  • interpret research that uses such methods and, when relevant, present well-founded critique of research papers
  • use an econometric software
  • plan and implement a small-scale empirical research project using causal inference methods.