Selection bias in evaluating treatment effects: Some formal illustrations

By A. Goldberger. 2008

127 citations recorded by the source corpus.

Subfields: Education, Statistics and Probability

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Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, positive): “We recommend using local linear or local quadratic methods (see for details on the implementation Hahn et al. [2001], Porter [2003], Calonico et al. [2014a]) rather than global polynomial methods.”
E. Regression Discontinuity Design: An Effectiveness-Based Evaluation of Five State Pre-Kindergarten Programs (2008, methodology): “A regression-discontinuity design (RDD) was used because it produces unbiased causal estimates both in theory (Goldberger, 1972a; 1972b; Robbins & Zhang, 1988) and empirical practice (Aiken, West, Schwalm, Carroll & Hsiung, 1998; Buddelmeyer & Skoufias, 2003; Black, Galdo, & Smith, 2005; summarized ”
Does the Size of the Legislature Affect the Size of Government? Evidence from Two Natural Experiments (2012, unknown): “A detailed discussion of efficiency of the regression-discontinuity method is provided in Goldberger (1972) 7 Conditional mean independence is also known as “selection on observables” or “ignorability of treatment””
Modern Regression Discontinuity Analysis (2012, background): “…represents the collinearity (or correlation squared) that exists between the treatment indicator and rating in a regression discontinuity design.20 This collinearity depends on how ratings are distributed around the cut-point (Bloom, Kemple, Gamse, & Jacob, 2005; Goldberger, 1972; Schochet, 2008).”
Regression-Discontinuity Analysis: A Survey of Recent Developments in Economics (2008, background): “As pointed out by Lee and Card (2008), in such a case it is no longer possible to compare observations just above or just below the cut-off because of the inevitable gap between discrete values.”
Power calculations for regression-discontinuity designs (2019, background): “See Imbens and Lemieux (2008); Lee and Lemieux (2010); Calonico, Cattaneo, and Titiunik (2015a); Cattaneo and Escanciano (2017); Cattaneo, Titiunik, and Vazquez-Bare (2017); Cattaneo, Idrobo, and Titiunik (Forthcoming a,b); and references therein for introductions to RD designs.”
Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables (2013, unknown): “Proven by Goldberger (2008) in 1972, the design has been shown empirically to produce effects akin to an experiment’s (Aiken, West, Schwalm, Carroll, & Hsuing, 1998; Berk, Barnes, Ahlman, & Kurtz, 2010; Black, Galdo, & Smith, 2007; Buddelmeyer & Skoufias, 2004; Shadish, Galindo, Wong, Steiner, & Coo”
A Practical Guide to Regression Discontinuity (2012, background): “Economists revived the approach (Goldberger, 1972, 2008; van der Klaauw, 1997, 2002; Angrist and Lavy, 1999), formalized it (Hahn, Todd, and van der Klaauw, 2001), strengthened its estimation methods (Imbens and Kalyanaraman, 2009), and began to apply it to many different research questions.”
Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within‐study comparisons (2008, methodology): “Moreover, experiments are more efficient than RD (Goldberger, 1972).”