Statistical Methods for Causal Inference

Overview

There is great interest among students and practitioners today to understand the causal mechanisms underlying major events. Identifying cause-and-effect relationships is important for impact evaluation and effective policy design. Such identification can help us answer questions like: "What causes an economic downturn?", "Does universal basic income reduce unemployment?" and "Does a carbon tax reduce greenhouse gas emissions?"

However, identifying causal relationships using data is often error prone. Differentiating causality from simple correlation requires learning and applying sophisticated quantitative tools. The golden standard of identifying causal linkages relies on designing experiments, often through randomised control trials. But designing a randomised control trial is not always feasible or ethical. Moreover, some events might have already happened in the past, such as a financial crisis or a cyclone. How can one use observational data to analyse the causal effects of such events?

Over two weeks, students in the Statistical Methods for Causal Inference course from Vrije Universiteit Amsterdam are introduced to experimental and quasi-experimental methods which allow them to infer cause-and-effect relationships robustly.

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