Causal Inference

Introduksjon

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.

Kursets innhold

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

Forbehold

Dette er et utdrag fra den komplette kursbeskrivelsen for kurset. Dersom du er aktiv student på BI, kan du finne de komplette kursbeskrivelsene med informasjon om bl.a. læringsmål, læreprosess, pensum og eksamen på portal.bi.no. Vi tar forbehold om endringer i denne beskrivelsen.