Empirical Strategies for Causal Analysis

ECO433 Empirical Strategies for Causal Analysis

Autumn 2021

Spring 2022
  • 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 randomized experiments as the predominant way for establishing causality. Last, we use the potential outcomes framework to discuss designs and methods for data from observational studies. In particular, designs and methods covered include instrumental variables, difference-in-difference, event study design, regression discontinuity design, kink design, and bunching. 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 (LATE)
    • Fixed effects and panel data
    • Differences-in-differences
    • Event study design
    • Regression discontinuity design
    • Kink design
    • Bunching

  • 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, STATA labs sessions, term paper and digital presentation (in groups).

    Note: lectures and lab sessions will be digitally available.

  • Required prerequisites

    We assume familiarity with linear regression at the level of the courses ECO402, or equivalent.

  • Requirements for course approval


  • Assessment

    The final grade will be based on a term paper including a digital presentation in groups (40%) and a final 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


  • Literature

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


ECTS Credits
Teaching language

Spring. Offered Spring 2021.

Course responsible

Assistant Professor Fanny Landaud, Department of Economics.