Statistical Methods for Causal Inference

About

This course introduces students to advanced statistical tools of causal inference for impact evaluation and policy analysis.

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?

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 on advanced master's level but also open to PhD students and working professionals, 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 also bring their own laptops to the course. Laptops should be capable of running R Studio.

Fee info

Students, PhD candidates and employees of VU Amsterdam, Amsterdam UMC or an Aurora Network Partner €765 Students and PhD candidates at partner universities of VU Amsterdam €1035 Students and PhD candidates at non-partner universities of VU Amsterdam €1140 Professionals €1360