Proxy Variables and Specification Bias

By P. Frost. 1979

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Citations in the corpus

Causal Effect Inference with Deep Latent-Variable Models (2017, neutral): “Proxy variables and the challenges of using them correctly have long been considered in the causal inference literature [54, 14].”
Policy Evaluation with Latent Confounders via Optimal Balance (2019, background): “Finally there has been a long history of work in causal inference using proxies for true confounders [11, 42].”
Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research (2020, background): “Becker, 2005; Bernerth & Aguinis, 2016; Breaugh, 2008; Frost, 1979; McCallum, 1972; Pei, Pischke, & Schwandt, 2019; Spector & Brannick, 2011 Sensitivity Analysis – Estimating the magnitude of the bias created by potential violations of exogeneity assumption by analyzing how the inclusion of control…”
Factors versus Composites: Guidelines for Choosing the Right Structural Equation Modeling Method (2019, background): “, Frost, 1979). In contrast, the SEM literature and SEM users typically assume equivalence between the conceptual variable and the common factor or the weighted composite carrying the same name. If research is inspired by a theoretical model featuring the conceptual variable team performance, for ex”
Poorly Measured Confounders are More Useful on the Left than on the Right (2017, background): “If xi is an imperfect control or there are multiple omitted variables in (1), then (2) does not necessarily reduce the omitted variables bias (Frost 1979 or more recently De Luca, Magnus, and Peracchi forthcoming and Kassenboehmer and Schurer 2018). The literatures along the lines of Altonji, Elder,”