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Treatment Evaluation Based on Instrumental Variables

Online via Zoom
General Topics:
Course Level:
Software used:
Students: 220 €
Academics: 330 €
Commercial: 660 €
Additional links
Lecturer(s): Martin Huber

About the lecturer - Martin Huber

Course description

Instrumental variable (IV) methods are a popular tool in the arsenal of causal inference, providing a mechanism to uncover the causal impact of a treatment, such as participation in training, on an outcome of interest, such as earnings. Despite potential confounding factors or error terms that may influence both the treatment and the outcome, IV methods enable researchers to isolate the treatment effect. This course is dedicated to the exploration of IV methods for nonparametric identification of the local average treatment effect (LATE). Beginning with an overview of the fundamental assumptions and concepts underlying instruments and the LATE, the course progresses to cover advanced topics, such as integrating machine learning algorithms, testing IV assumptions, and conducting sensitivity analyses. Specifically, the following topics are covered:
- Introduction to the concept of instrumental variables
- Identification of the local average treatment effect (LATE)
- Identification of potential outcome distributions (including means)  
- Treatment evaluation under conditional instrument validity (when controlling for covariates)
- Causal machine learning methods for LATE estimation and effect heterogeneity analysis
- Marginal treatment effects (based on multiple/continuous instruments)
- Verifying external validity of the LATE
- Testing the LATE assumptions
- Relaxing the LATE assumptions
- Sensitivity checks of the LATE assumptions
- Identification based on rank assumptions
Besides the econometric/statistical concepts of the various topics, the course also presents empirical applications from the social sciences and demonstrates the practical implementation of several methods in the statistical software package “R”. An introduction to “R”, which is free of charge, can be found here:
Organizational structure of the course
Online lectures with group discussions of the topics are followed by online PC sessions with data applications using the statistical software R. During the PC sessions, the lecturer will be available for individual support and consultations.

Target group

Participants will find the course useful if:
  • They are familiar with basic concepts of causal inference and would like to gain or deepen their knowledge about instrumental variable methods and keep up with recent developments.

Learning objectives

By the end of the course, participants will:
  • understand the intuition, strengths, and potential shortcomings of various instrumental variable approaches,
  • be able to apply instrumental variable methods to real-world data using the statistical software R.

  • Prerequisites

  • Basic knowledge of statistics (e.g., regression analysis) and causal inference (e.g., potential outcome notation or DAGs).
  • Basic knowledge of the statistical software R (e.g., for data management and analysis).
    Software requirements
    Statistical software R and its interface R Studio must be installed and ready to use on the participants' PCs or laptops.


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