Causal Inference with Big Data

Introduction

In the 21st century, information is created and stored at unprecedented rates. The access to high-dimensional large data sets – “Big Data” – has opened up new possibilities for business analytics and economic research. Massive datasets alone are, however, insufficient to answer fundamental questions within business and economics. Using the potential outcome framework, we explore various methods useful for causal inference in the Big Data era. We discuss the promise and pitfalls of large-scale experimentation, machine learning, and regression discontinuity designs. We consider empirical applications relevant for business and policy analysis.

Course content

The course covers the following topics:

• What is big data?
• The potential outcome framework
• Regression and matching
• Large-scale experimentation
• Treatment effect heterogeneity
• False positives and p-hacking
• Publication bias
• Regression discontinuity designs
• Supplementary analysis
• Data visualization