Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel
Orthogonal Learner
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel
Orthogonal Learner
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random …