Normalizing Flows for Interventional Density Estimation

Understand

Existing machine learning methods for causal inference usually estimate quantities expressed via the mean of potential outcomes (e.g., average treatment effect).

• However, such quantities do not capture the full information about the distribution of potential outcomes.
• In this work, we estimate the density of potential outcomes after interventions from observational data.
• For this, we propose a novel, fully-parametric deep learning method called Interventional Normalizing Flows.