Instrumental Variables

By Michael P. Murray. 2011

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Citations in the corpus

Causal inference by using invariant prediction: identification and confidence intervals (2015, methodology): “Instrumental variables can sometimes be used when the causal relationship of interest is confounded and there are no randomised experiments available [Bowden and Turkington, 1990, Angrist et al., 1996, Wright, 1928].”
Causal inference in statistics: An overview (2009, background): “(31) is known as the instrumental variable estimand (Bowden and Turkington, 1984).”
Anchor regression: Heterogeneous data meet causality (2018, methodology): “Furthermore, as the proposed procedure interpolates between the solution to ordinary least squares and the instrumental variables (two-stage least squares) approach, there are obvious connections to the IV literature, see e.g., [Wright, 1928, Bowden and Turkington, 1990, Didelez et al., 2010].”
Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions (2017, background): “If an instrumental variable is available, then diﬀerent forms of instrumental variable regression [Wright, 1928, Bowden and Turkington, 1990, Angrist et al., 1996, Didelez et al., 2010] can be used to infer the causal eﬀect of a single variable on a target of interest.”
Instrumental Variables: An Econometrician's Perspective (2014, unknown): “Key references to this literature are Brown (1983), Roehrig (1988), Newey and Powell (2003), Chesher (2003, 2010), Benkard and Berry (2006), Matzkin (2003, 2007), Altonji and Matzkin (2005), Imbens and Newey (2009), Hoderlein and Mammen (2007), Horowitz (2011) and Horowitz and Lee (2007).”
Detecting Confounding in Multivariate Linear Models via Spectral Analysis (2017, background): “Moreover, we assume that there are no observed causes ofX that could act as so-called instrumental variables [4] which would enable the distinction between ‘causal’ and ‘confounded’.”
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example (2014, methodology): “To deal with the presence of unmeasured confounders of observed random variables the so-called instrumental variable technique [2] has found applications in genetics [3], epidemiology [4, 5] and economics [6].”
The Perils of Unearned Foreign Income: Aid, Remittances, and Government Survival (2012, background): “Moreover, the F-test on the instrument of 17 easily exceeds the conservative threshold for weak instruments of 10 suggested by Staiger and Stock (1997).”
Measurement bias and effect restoration in causal inference (2014, background): “Especially, when S is an empty set, Z is called an instrumental variable (Bowden & Turkington, 1984).”
Frugal IV alternatives to identify the parameter for an endogenous regressor (2009, methodology): “The LIV and HM estimator do not require such additional information, but are as a consequence more 5 The LIV approach has some features that are similar to the grouping method developed by Wald (Wald, 1940; Madansky, 1959; Bowden and Turkington, 1984).”
Use of Quasi-Experimental Research Designs in Education Research: Growth, Promise, and Challenges (2020, background): “pdf Bettinger, E. (2010). Instrumental variables. In B. McGaw, P. Peterson, & E. Baker (Eds.), International encyclopedia of education (3rd ed., Vol. 8). Elsevier. Callahan, M. K., Meehan, K., & Shaw, K. M. (2017). Impact of OBF on student outcomes: Tennessee and Indiana. Research for Action. Campbe”