Undersmoothing Causal Estimators with Generative Trees

Understand

Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions.

• It is, however, hard to infer these effects from observational data.
• One major problem that can arise is covariate shift where the data (outcome) conditional distribution remains the same but the covariate (input) distribution changes between the training and test set.
• In an observational data setting, this problem is materialised in control and treated units coming from different distributions.