Causal Inference with Transfer Learning: Calibrating Treatment Effects via Distributional NCOs

Music City Center 

We propose a novel framework for causal inference that utilizes transfer learning methodologies to estimate the average treatment effect (ATE) in a target population where the primary outcome and negative control outcomes (NCOs) are unobserved. Our approach leverages a source dataset, which contains both the primary outcome and NCOs, to predict individual-level NCOs in the target dataset and calibrate the treatment effect estimate. Specifically, we develop a model that transfers information from the source population to the target population, using NCOs as a bias correction tool to enhance causal validity. We establish the identifiability conditions for our approach and derive implications for the observed data distribution. Through simulation studies, we demonstrate that our method accurately recovers the true ATE and improves bias correction with pseudo NCOs-calibration. To illustrate its practical utility, we apply our method to evaluate the impact of GLP-1 receptor agonists on mental health disorders across multiple clinical sites. Our findings highlight the potential of transfer learning-based causal inference in addressing challenges posed by incomplete outcome data setting.

Causal inference


Transfer learning


Digital twin

Negative control outcomes

Bias calibration

Average treatment effect 

Biopharmaceutical Section