Empirical Strategies for Causal Analysis

ECO433 Empirical Strategies for Causal Analysis

Spring 2024

Autumn 2024
  • Topics

    In most of economics, marketing and business management, we are interested in causal relationships between variables, rather than mere correlations. For example, it is not the correlation between marketing expenses and sales that is of interest, but the effect of increasing marketing expenses for a product on the sale volume of the same product. In this course, we study methods for estimating and identifying such causal effects.

    First, the course provides a brief review of basic regression techniques. Second, we introduce the topic of causal analysis. We will define causal effects based on the potential outcomes framework, encounter the fundamental problem of causal analysis, and discuss what separates association from causation. In the third part of the course, we discuss designs and methods for data from observational studies including instrumental variables, difference-in-difference, event study design, regression discontinuity design, and kink design. Examples from the literature and step-by-step tutorials offer hands-on experiences in utilizing the methods.

    Preliminary course outline:

    • Short review of basic regression techniques (inference, asymptotics and dummy variables)
    • Causal inference using potential outcomes
    • Randomized experiments
    • Regression and causality
    • Instrumental variables
    • Fixed effects and panel data
    • Differences-in-differences and event study design
    • Regression discontinuity design
    • Kink design

  • Learning outcome


    At the end of the course students will:

    • have a sound knowledge on different methods for causal analysis   
    • understand how empirical methods can be used for testing the implications of theoretical models and interpret the estimation results
    • understand the assumptions necessary to estimate causal effects


     At the end of the course students will:

    • be equiped with the intuition and skills necessary to understand and to apply methods of causal analysis to actual observational and experimental data
    • be able to formulate a research questions
    • be able to critically assess reports discussing associations between variables and interpret causal effects
    • know how to write and run do-files with relevant commands and produce tables and figures in STATA

    General Competence

     At the end of the course students will:

    • be able to independently estimate causal effects for instance as a part of a master thesis or in future professional careers

  • Teaching

    Plenary lectures, labs sessions, term paper and presentation (in groups).

  • Required prerequisites

    We assume familiarity with linear regression at the level of the courses ECN402, or equivalent.

  • Compulsory Activity


  • Assessment

    The final grade will be based on a term paper including a presentation in groups (40%) and a final individually written home exam (3 hours) (60%). The term paper and exam must be written in english.

    The term paper covering a topic related to the course must be submitted in the week of course completion.

  • Grading Scale

    Grading scale A - F.

  • Computer tools

    STATA, R

  • Literature

    Angrist and Pischke (2014). "Mastering Metrics - the Path from Cause to Effect", Princeton University Press.


ECTS Credits
Teaching language

Spring. Will not be offered spring 2024.

Course responsible

Professor Aline Bütikofer, Department of Economics (main course responsible).

Assistant Professor Andreas Haller, Department of Economics.