When combining RCT and observational data with different measured variables, learning a shared embedding space and calibrating predictions outperforms traditional imputation methods, especially for detecting non-linear treatment effects.
This paper solves a practical problem in medical research: combining data from randomized trials (which prove causation but have small samples) with observational studies (which have large samples but measure different variables).