Causal Inference

Introduction

Sound empirical analysis rests on disciplined research design rather than on statistical tools alone. Whether assessing the impact of a new business strategy or evaluating a policy intervention, analysts must carefully structure their approach to separate causal effects from confounding factors. This course introduces the potential outcome framework and equips students with experimental and quasi-experimental methods that enable credible causal reasoning in applied settings.

Course content

The course covers the following topics:

• Research design and causality
• The potential outcome framework
• Experimental methods in practice: lab, field, and A/B testing
• Regression adjustment and matching methods
• Instrumental variable methods and non-compliance in experiments
• Heterogeneous treatment effects
• Regression discontinuity designs
• Difference-in-differences and event studies
• False positives, p-hacking and publication bias
• Supplementary analysis and replication

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.