Estimating individual treatment effects under unobserved confounding using binary instruments

Understand

Estimating conditional average treatment effects (CATEs) from observational data is relevant in many fields such as personalized medicine.

• However, in practice, the treatment assignment is usually confounded by unobserved variables and thus introduces bias.
• A remedy to remove the bias is the use of instrumental variables (IVs).
• Such settings are widespread in medicine (e.g., trials where the treatment assignment is used as binary IV).