Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions

By Dylan S. Small. 2007

165 citations recorded by the source corpus.

Subfields: General Agricultural and Biological Sciences, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c03696ab611fa097d74976f7dc25efc9a829db2a

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In General Agricultural and Biological Sciences

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder (2009, background): “Such a union of intervals is itself an interval: following Rosenbaum (2002), we call it a sensitivity interval; and following Small (2007), we refer to the determining set of permissible values for (tW ,ρ2y·w|zx) as a sensitivity zone.”
Effect Modification and Design Sensitivity in Observational Studies (2013, background): “4); Manski (1990); Gastwirth (1992); Marcus (1997); Imbens (2003); Altonji, Elder, and Taber (2005); Yu and Gastwirth (2005); Small (2007); Ichino, Mealli, Jesse Y. Hsu is Post-doctoral fellow, Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340,…”
Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference* (2014, methodology): “Sensitivity analysis methods for IV analyses have been developed in [9,43,94,101,102] among others.”
Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization (2014, methodology): “[132] Kolesár, M., Chetty, R., Friedman, J. N., Glaeser, E. L., and Imbens, G. W. (2011), “Identification and Inference With Many Invalid Instruments,” NBER Working Paper No. 17519, National Bureau of Economic Research.”
Instrumental Variables: An Econometrician's Perspective (2014, unknown): “An exception is Small (2007). Obviously it is often difficult in applications to find even a single variable that satisfies the conditions for it to be a valid instrument.”
Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of For-Profit and Not-For-Profit High Schools in Chile (2014, methodology): “For other models for sensitivity analysis in observational studies, see Gastwirth (1992), Hosman, Hansen and Holland (2010), Marcus (1997), Rosenbaum and Rubin (1983), Small (2007), Yanagawa (1984) and Yu and Gastwirth (2005).”
Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression (2015, methodology): “In IV analysis more generally, a heterogeneity test is equivalent to an over-identification test, often performed with individual-level data as part of a TSLS analysis.(48) Another problem with the use of many genetic variants is that of weak instruments.”
Identification With Imperfect Instruments (2008, unknown): “(See, for example, Angrist, Imbens, & Rubin, 1996; Imbens, 2003; Small, 2007; Rosenbaum & Small, 2008.”
War and Wages (2008, methodology): “An alternative approach to sensitivity analysis with an instrumental variable was developed by Small (2007).”
Confidence intervals for causal effects with invalid instruments by using two‐stage hard thresholding with voting (2016, unknown): “We consider the additive linear, constant effects model of Holland (1988) and extend it to allow for possibly invalid instruments as in Small (2007) and Kang et al. (2016b).”
DOUBLY ROBUST INSTRUMENTAL VARIABLE REGRESSION (2012, methodology): “An additive, linear constant-effect causal model for the potential outcomes (as in Holland (1988) and Small (2007)) is Y (W ∗) i = Y (0) i + α0W ∗.”
Beyond plausibly exogenous (2018, unknown): “1 Alternative approaches to dealing with violations of the exclusion restriction include Ashley (2009), Berkowitz et al. (2012), Flores and Flores-Lagunes (2012), Hahn and Hausman (2002), Jones (2015), Kang et al. (2016), Kolesár et al. (2015), Kraay (2012), Nevo and Rosen (2012) and Small (2007).”