On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9

By J. Spława-Neyman, D. Dabrowska, T. Speed. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Metalearners for estimating heterogeneous treatment effects using machine learning (2017, methodology): “Framework and Definitions We use the Neyman–Rubin potential outcome framework (11, 12) and assume a superpopulation or distribution P from which a realization of N independent random variables is given as the training data.”
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.”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, background): “The notion of potential outcomes was introduced by Neyman (1923) for randomized experiments and by Rubin (1974a) for nonrandomized studies.”
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2018, methodology): “Conditions (23)–(24) amount to requiring orthogonality to hold at whatever limits (ᾱ1, γ̄), which can be achieved by carefully choosing (“calibrating”) the loss functions for the corresponding estimators.”
TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, unknown): “Equation (4) constitutes the bridge between the structural interpretation of counterfactuals and the potential outcome framework advanced by Neyman (1923) and Rubin (1974), which takes the controlled randomized experiment as its guiding paradigm (see Appendix 1).”
The Augmented Synthetic Control Method (2018, methodology): “We adopt the potential outcomes framework (Neyman 1923) and invoke SUTVA, which assumes a well-defined treatment and excludes interference between units; the potential outcomes for unit i in period t under control and treatment are Y it ( 0 ) and Y it ( 1 ) , respectively.”