Mean-Squared-Error Calculations for Average Treatment Effects

By Guido W. Imbens, Whitney K. Newey, Geert Ridder. 2005

94 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits (2016, background): “Under such consistency assumptions, it has been shown that DM and/or DR are optimal (Imbens et al., 2007; Li et al., 2015; Rothe, 2016),1 whereas standard IPS is not (Hahn, 1998; Li et al.”
Nonparametric Tests for Treatment Effect Heterogeneity (2008, neutral): “[4]”
The Econometrics of Data Combination (2007, methodology): “This estimator is similar to the estimator considered in Chen, Hong and Tarrozi (2004) and Imbens, Newey, and Ridder (2004), and their results can be used to analyze this estimator.”
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2013, unknown): “Standard semiparametric analses, such as Hirano, Imbens, and Ridder (2003), Imbens, Newey, and Ridder (2007), or Cattaneo (2010), can be viewed in this context as oracle models that know in advance which terms yield the best approximation, typically assumed to be the first terms.”
Efficient semiparametric estimation of multi-valued treatment effects under ignorability (2010, unknown): “) In the context of program evaluation and for the particular case of binary treatments, great effort has been devoted to the efficient estimation of the Average Treatment Effect (ATE) and Average Treatment Effect on the Treated using either nonparametric regression methods (Hahn, 1998; Heckman et a”
Econometric Methods for Program Evaluation (2018, methodology): “Imputation/Projection Methods Imputation/projection methods (e.g., Heckman et al., 1998; Little and Rubin, 2002; Imbens et al., 2006; Cattaneo and Farrell, 2011) provide an alternative class of estimators that rely on the assumptions in Equations (11) and (12).”
Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT (2014, background): “To our knowledge, there is no well-developed theory to guide the power series choice in finite samples (though Imbens, Newey, and Ridder 2007 is a step in this direction); hence, a reasonable strategy in practice would involve examining the sensitivity of results to various specifications as is…”
Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand (2006, unknown): “The first data set we use is a data set originally put together by Lalonde (1986), and subsequently used by Dehejia and Wahba (1999) and Smith and Todd (2004).”
Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models (2007, methodology): “Hahn (1998) and Imbens et al. (2005) used power series and splines in the two-step efficient estimation of the average treatment effect models.”
Bias-Corrected Matching Estimators for Average Treatment Effects (2011, methodology): “Various forms of such estimators were proposed by Hahn (1998), Heckman et al. (1998), Chen, Hong, and Tarozzi (2008), and Imbens, Newey, and Ridder (2005).”
Efficient Estimation of the Dose–Response Function Under Ignorability Using Subclassification on the Covariates (2011, methodology): “For a discussion of these alternative, well-known approaches see, e.g., Chen, Hong and Tarozzi (2004, 2008), Bang and Robins (2005), Imbens, Newey, and Ridder (2006), Tsiatis (2006), Heckman and Vytlacil (2007), Imbens and Wooldridge (2009), and references therein.”