A Distributional Approach for Causal Inference Using Propensity Scores

By Zhiqiang Tan. 2006

346 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning (2019, methodology): “[24] Z.”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, background): “for arbitrary h(x), which is well known from Robins, Rotnitzky and Zhao (1994). If model III is correct, then m0(x) = m(x,β) and π0(x) = π(x,α) for some β and α, and influence functions for estimators μ̂ have the form”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, background): “After we drafted our article, a number of important works on DR estimators appeared, including Tan’s (2006) article on causal inference, the monograph by Tsiatis (2006) and the recent articles and technical reports cited by Robins, Sued, Lei-Gomez and Rotnitzky. The discussants’ insightful remarks h”
Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap (2017, methodology): “However, Tan (2006) did not consider sampling variation of the bounds; thus his method has limited applicability in practice.”
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2018, methodology): “For previous analyses, propensity score and outcome regression models were employed either with main effects only (Hirano and Imbens (2002); Vermeulen and Vansteelandt (2015)) or with interaction terms manually added (Tan (2006)).”
Regression and Weighting Methods for Causal Inference Using Instrumental Variables (2006, methodology): “These two approaches are parallel to those in the case of unconfounded assignment mechanism (1) (see Tan 2006 and references therein).”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “Another important case is when D is an indicator for a different level of treatment. Yet another possibility is when D indicates a subpopulation formed after the treatment assignment. For example, in the evaluation of a job training program, some individuals might choose to obtain additional educati”
Marginal and Nested Structural Models Using Instrumental Variables (2010, methodology): “Robins 1999b; Tan 2006b ). This connection re∞ects the fact that Z is an experimental handle”
Bounded, efficient and doubly robust estimation with inverse weighting (2010, neutral): “…intrinsically efficient: if the propensity score model is correctly specified, then each such estimator is asymptotically efficient among a class of augmented inverse-probability-weighted estimators that use the same fitted outcome regression function as the estimator itself does (Tan, 2006, 2007).”
Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data (2017, methodology): “In previous analyses using propensity scores, logistic regression was employed either with main effects only (e.g., Hirano & Imbens, 2002; Vermeulen & Vansteelandt, 2015) or with interaction terms manually added (Tan, 2006) in the approach of Rosenbaum & Rubin (1984).”
Entropy Balancing is Doubly Robust (2015, background): “…popular in survey sampling (Deville and Särndal, 1992; Särndal and Lundström, 2005; Chan et al., 2015); (2) The empirical likelihood approach that significantly advances the theory of doubly robust estimation in observation study (Wang and Rao, 2002; Tan, 2006; Qin and Zhang, 2007; Tan, 2010).”
Estimation of controlled direct effects (2008, methodology): “Alternatively, we may improve the finite-sample behavior of ψ̂DR by adapting ideas in Tan (2006) for inverse weighting estimators to inverse weighting estimating functions.”
Bias-Reduced Doubly Robust Estimation (2015, background): “To accommodate this, arguing along the lines of Tan (2010), we define the function”
Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST) (2016, neutral): “In the context of average treatment effect estimation, Tan (2006) calibrated estimates of the two potential outcome distributions to features of the empirical distribution of always observed variables (see Qin and Zhang 2007; Graham, de Xavier Pinto, and Egel 2012).”
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, methodology): “Similar results appear in Birmingham, Rotnitzky, and Fitzmaurice (2003), Robins, Rotnitzky, and Scharfstein (2000), Tan (2006), Graham (2011), Hristache and Patilea (2017), Franks, Amour, and Feller (2020), and Zhao, Small, and Bhattacharya (2019).”