Use of the logistic model in retrospective studies.

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Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018, methodology): “The direct estimation approach computes the counterfactuals by learning a mapping from feature-treatment pair to their outcomes (Prentice 1976; Wager and Athey 2015).”
Learning Representations for Counterfactual Inference (2016, background): “Another commonly sought after quantity is the average treatment effect , ATE = E x ∼ p ( x ) [ ITE ( x )] for a population with distribution p ( x ) .”
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017, methodology): “Examples include any type of regression model such as linear and logistic regression, random forests and regression trees [12, 33, 42].”
Constructing effective personalized policies using counterfactual inference from biased data sets with many features (2016, methodology): “The direct methods compute counterfactuals by learning a function mapping from feature-action pair to rewards [Prentice(1976), Wager and Athey(2015)].”