Regression and Weighting Methods for Causal Inference Using Instrumental Variables

By Zhiqiang Tan. 2006

187 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Education, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/003df47000a59630d170364ad4dd80a2b912c891

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Citations in the corpus

Interpretable Almost Matching Exactly With Instrumental Variables (2019, methodology): “Other IV methods in the presence of measured covariates include Bayesian methods (Imbens and Rubin, 1997), semiparametric methods (Abadie, 2003; Tan, 2006; Ogburn et al., 2015), nonparametric methods (Frölich, 2007) and deep learning methods (Hartford et al., 2017), but these methods do not enjoy…”
Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions (2013, unknown): “Robins, Rotnitzky, and Zhao, 1994; Robins and Rotnitzky, 1995), average treatment effects in potential outcome models with unconfounded assignment (Scharfstein, Rotnitzky, and Robins, 1999), and local average treatment effects in instrumental variable models (Tan, 2006), amongst many others.”
Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables (2014, methodology): “The LATE is estimated by weighting based on the (parametric) instrument propensity score; see Frölich (2007) and Tan (2006).”
War and Wages (2008, methodology): “In this structure, there can be substantial selection bias in accepting the treatment, but this can removed through an instrumental variables analysis, as was carefully developed by Angrist, Imbens, and Rubin (1996) (see also Sommer and Zeger 1991; Frangakis and Rubin 1999; Tan 2006).”
DOUBLY ROBUST INSTRUMENTAL VARIABLE REGRESSION (2012, methodology): “Alternative doubly robust estimators are proposed by Tan (2006a, 2010a) to improve efficiency when the propensity score model is correctly specified but the outcome regression model is misspecified.”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “More precisely, and similarly to Tan (2006b), we can identify this parameter of interest as τh,late = E [ Zh sh (X) Y − Z0s0(X)Y ] E [ Zh sh (X) W − Z0s0(X)W ] , (27) where both the numerator and the denominator are simply equal to the average effects of Zh , compared with Z0, on the outcome and…”
Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions (2007, background): “For assumption (A2) to be plausible, it is often necessary to condition on a vector of covariates xi (Tan 2006).”
Marginal and Nested Structural Models Using Instrumental Variables (2010, methodology): “and sibs jointly and use the following discretization in Tan (2006a) .”
Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference* (2014, methodology): “[64] developed a parametric approach; Abadie [66] and Tan [67] developed semiparametric approaches.”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, background): “There are also doubly robust estimators available for different settings including instrumental variables estimation (Tan, 2006; Okui, Small, Tan, and Robins, 2012) and estimation under multivalued treatments (Uysal, 2015).”
Instrumental Variables: An Econometrician's Perspective (2014, unknown): “…Robins (2006), Robins (1994), Robins and Rotnitzky (2004), Vansteelandt and Goetghebeur (2003), Vansteelandt et al. (2011), Hirano et al. (2000), Tan (2006, 2010), Abadie (2002, 2003), Duflo, Glennester and Kremer (2007), Brookhart et al. (2006), Martens et al. (2006), Morgan and…”