Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects

By Xiaohong Chen, Han Hong, Alessandro Tarozzi. 2008

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Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “For causal inference, Chen, Hong, and Tarozzi (2008) and Athey, Imbens, and Wager (2018) obtain efﬁciency under weaker conditions than ours on p( x ) (the former under minimal smoothness on µ t ( x ) and the latter under a sparsity in a high-dimensional linear model).”
Nonparametric Tests for Treatment Effect Heterogeneity (2008, neutral): “Various estimators have been proposed for the average treatment eﬀect in this setting, e.g., Hahn (1998), Heckman, Ichimura and Todd (1998), Hirano, Imbens and Ridder (2003), Chen, Hong, and Tarozzi (2004), and Abadie and Imbens (2006).”
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).”
Bias From Censored Regressors (2009, background): “xi is exogenously censored, by assuming that E ("ijdi;xi;wi) = 0 ( 4 )”
Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST) (2016, methodology): “In principle, if the dimension of the approximating model is allowed to grow with the sample size, the Chen, Hong, and Tarozzi (2008) estimator is consistent for, and efficient under, all data-generating processes satisfying parts (i) to (iv) of Assumption 1.”
Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models (2007, methodology): “Chen et al. (2004a) studied sieve MLE of semi-nonparametric multivariate copula models.”
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2013, unknown): “We first discuss treatment effect inference with a general model selector.”
Oaxaca-Blinder as a Reweighting Estimator (2011, background): “9See Robins, Sued, Lei-Gomez, and Rotnitzky (2007) and Chen, Hong, and Tarozzi (2008) for further discussion of this issue. the O-B estimate of the counterfactual mean in matrix notation as: ̂10 1 N1 D0HY H X X0SX 1 X0S where Y is the N 1 vector of outcomes, D is an N 1 vector whose elements…”
EMPIRICAL LIKELIHOOD FOR ESTIMATING EQUATIONS WITH MISSING VALUES (2009, methodology): “which is the semiparametric efficiency bound for the estimation of θ as given by Chen, Hong and Tarozzi [4].”
Efficient semiparametric estimation of multi-valued treatment effects under ignorability (2010, unknown): “…indicator is binary that involves the parametric estimation of both a regression function and the propensity score, while Chen et al. (2005) and Chen et al. (2008) study efficient GMM estimation in the context of (non-classical) measurement error models when two samples are available (i.e.,…”
Estimation of Multivalued Treatment Effects under Conditional Independence (2013, methodology): “This identification discussion is associated with the ideas of projection and imputation, which could be used to construct (multivalued) treatment effect estimators (see, e.g., Hahn (1998), Imbens et al. (2006), (Chen et al. (2004), Chen et al. (2008)) and Cattaneo and Farrell (2011)).”