[On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies

By Donald B. Rubin. 1990

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Citations in the corpus

Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms (2018, methodology): “The proposed method involves the estimation of coun-terfactuals, which can be formalized using the Neyman-Rubin potential outcomes framework (Neyman, 1923; Rubin, 1974).”
The Econometrics of Randomized Experiments (2016, methodology): “Here we follow the line of research that originates in the work by Neyman (1929, 1935, 1990).”
Causal inference in statistics: An overview (2009, methodology): “Those versed in the potential-outcome notation (Neyman, 1923; Rubin, 1974; Holland, 1988), can recognize such expressions through the subscripts that are attached to counterfactual events and variables, e.g. Yx(u) or Zxy.”
Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions (2017, background): “In the context of structural equation models [Bollen, 1989, Robins et al., 2000, Pearl, 2009], one possibility is to characterize the Markov equivalence class of graphs under the assumption of acyclicity and usually faithfulness [Verma and Pearl, 1991, Andersson et al., 1997, Tian and Pearl, 2001,…”
On Partial Identification of the Pure Direct Effect (2015, background): “This strand of work is based on ideas originating from Robins and Greenland (1992) and Pearl (2001) grounded in the language of potential outcomes (Splawa-Neyman et al., 1990; Rubin, 1974, 1978) to give nonparametric definitions of effects involved in mediation analysis, allowing for settings where ”
Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the School Voucher System in Chile (2014, background): “and the other observed under control Zsj = 0 (Neyman 1923; Rubin 1974).”
The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility (2018, unknown): “Growing up in a better neighborhood is beneficial throughout childhood, but where children live as adolescents (rather than at very early ages) is particularly influential in determining their later outcomes, consistent with Deutscher’s (5)As is now conventional in the social sciences, we use a pote”
Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign (2011, background): “For the sake of simplicity, we assume no interference between voters (Cox 1958; Rubin 1990).”
Conformal inference of counterfactuals and individual treatment effects (2020, methodology): “Throughout the paper, we focus on the potential outcome framework (Neyman, 1923/1990; Rubin, 1974) with a binary treatment.”
A Survey on Causal Inference (2020, methodology): “To solve these problems in causal inference from observational data, researchers develop various frameworks, including the potential outcome framework [111, 129] and the structural causal model [89, 92, 94].”
Race and Economic Opportunity in the United States: an Intergenerational Perspective* (2018, unknown): “Section (7)As is now conventional in much of the social sciences, we use a potential outcomes framework (Neyman 1923; Holland 1986) to define notions of causality.”
Estimation of counterfactual distributions using quantile regression (2006, methodology): “Oaxaca (1973) and Blinder (1973) assume that the expected value of Y conditionally on X is a linear function of X.”