Statistical Methods for Causal Inference

About

This course provides a hands-on introduction to statistical methods for causal inference. Over two weeks, students are introduced to experimental and quasi-experimental methods which allow them to infer cause-and-effect relationships robustly. We teach these methods from both a theoretical and applied lens, supplementing lectures with hands-on computer tutorials in the R programming language to help students learn by doing.

Course leader

Dr. Sanchayan Banerjee & Jack Fitzgerald

Target group

This course is taught at master's level but also open to PhD students across all disciplines in quantitative social sciences. These include business, criminology, economics, econometrics, education, environmental sciences, finance, health sciences, international studies, psychology, public policy, political science, social policy, sociology, and statistics, all broadly defined.

Participating students are expected to have prior knowledge of regression analysis and hypothesis testing. If you do not have this knowledge, you can still participate in this course by additionally following the VU Amsterdam Summer School course Data Analysis in R in a previous session. Prior coding experience specifically in R is preferred but is not a prerequisite of the course.

All students must bring their own laptops to the course. Laptop should be capable of running R Studio

Course aim

By the end of this course, students will be able to:

Understand the difference between correlation and causation.
Apply quantitative methods of statistical data analysis to infer causal relationships.
Identify confounding factors that threaten causal inference and hamper the internal and external validity of analytical findings.
Critically analyse data using statistical methods like experiments, matching analysis, difference-in-differences, regression discontinuity, and instrumental variables estimation.
Explore challenges and limitations in the use of quantitative methods of causal inference such as data availability, missing data, and measurement errors.
Apply diagnostic knowledge to inform impact evaluations and develop evidence-based policies

Fee info

Student or PhD candidate: €1250, student or PhD candidate at any Dutch university or partner university of VU Amsterdam: €1125 Student, PhD candidate or employee of VU Amsterdam, Amsterdam UMC, or an Aurora Network Partner: €938, Non-student: €1500