Abstract
Estimators weighting observed outcomes to form an effect estimate have a long tradition in causal inference. The corresponding outcome weights are utilized in established procedures to check covariate balancing, to characterize target populations, or to detect and manage extreme weights. This paper provides a general framework to derive the functional form of such weights. It reveals when and how numerical equivalence between an original estimator representation as moment condition and a unique weighted representation can be established. The framework is applied to derive novel outcome weights for the leading cases of double machine learning and generalized random forests, and recovers existing results as special cases. The analysis highlights that implementation choices determine (i) the availability of outcome weights and (ii) their properties.
Local Organizer: Giovanni Angelini