A Structural Approach to Selection Bias

By Miguel A. Hern'an, S. Hernández-Díaz, James M. Robins. 2004

2,699 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

External Validity: From Do-Calculus to Transportability Across Populations (2014, background): “Manski (2007) defines “external validity” as follows: “An experiment is said to have “external validity” if the distribution of outcomes realized by a treatment group is the same as the distribution of outcome that would be realized in an actual program.”
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2019, methodology): “Hernán et al. (2004) propose a common structure for various types of selection bias.”
A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E) (2024, positive): “Bias in selection of participants into the study (or into the analysis) is the first of two domains that address selection bias, which arises when we condition, in the study design or in the analysis, on common effects of exposure and outcome (Hernán and Robins, 2020; Hernán et al., 2004).”
Understanding and Controlling User Linkability in Decentralized Learning (2018, background): “d shortly verify) is consistent among both the users’ prior data (known to adversary) and private device data (unknown to adversary). This implicit user selection biases arise from behavioral factors [11,26,38] that results in subtle variations of how humans 4 0.75 0.80 0.85 0.90 0.95 1.00 Intra-use”
The Hazards of Hazard Ratios (2010, methodology): “This built-in selection bias of the HR has also been described using causal diagrams.(3,4) In short, the average HR may be uninformative because of potentially time-varying period-specific HRs, and because the period-specific HRs may be time-varying because of built-in selection bias.”