The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods

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Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, methodology): “Such techniques include differences-in-differences models [5], the potential outcomes framework of the Rubin causal model [88] and the structural equation model [78, 83].”
The Book of Why: The New Science of Cause and Effect† (2019, methodology): “Many researchers rely on the Neyman (or Neyman–Rubin) potential outcomes model, which is discussed in Neyman (1923/1990), Rubin (1974, 2005) and Sekhon (2008).”
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “From observational data, since we can only observe one treatment regime and one final outcome Y for each patient, we apply Neyman-Rubin’s causal model (Sekhon, 2008) to define potential outcomes for the unobserved counterfactuals.”
The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being (2016, unknown): “The estimates are therefore considered to be ‘‘Intention To Treat’’ (ITT) estimates, which allow us to interpret the results as conservative estimates of the true effects.(22,23) By doing so, we do not need to make any statistical corrections in data to compensate for the lack of compliance.”