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Author: Tan, Zhiqiang
Resulting in 2 citations.
1. Sun, Baoluo
Tan, Zhiqiang
High-Dimensional Model-Assisted Inference for Local Average Treatment Effects With Instrumental Variables
Journal of Business and Economic Statistics published online (27 September 2021): DOI: 10.1080/07350015.2021.1970575.
Also: https://www.tandfonline.com/doi/full/10.1080/07350015.2021.1970575
Cohort(s): Young Men
Publisher: American Statistical Association
Keyword(s): Educational Returns; Modeling, Instrumental Variables; Statistical Analysis

Consider the problem of estimating the local average treatment effect with an instrument variable, where the instrument unconfoundedness holds after adjusting for a set of measured covariates. Several unknown functions of the covariates need to be estimated through regression models, such as instrument propensity score and treatment and outcome regression models. We develop a computationally tractable method in high-dimensional settings where the numbers of regression terms are close to or larger than the sample size. Our method exploits regularized calibrated estimation for estimating coefficients in these regression models, and then employs a doubly robust point estimator for the treatment parameter. We provide rigorous theoretical analysis to show that the resulting Wald confidence intervals are valid for the treatment parameter under suitable sparsity conditions if the instrument propensity score model is correctly specified, but the treatment and outcome regression models may be misspecified. In this sense, our confidence intervals are instrument propensity score model based, and treatment and outcome regression models assisted. For existing high-dimensional methods, valid confidence intervals are obtained for the treatment parameter if all three models are correctly specified. We evaluate the proposed method via extensive simulation studies and an empirical application to estimate the returns to education. The methods are implemented in the R package RCAL.
Bibliography Citation
Sun, Baoluo and Zhiqiang Tan. "High-Dimensional Model-Assisted Inference for Local Average Treatment Effects With Instrumental Variables." Journal of Business and Economic Statistics published online (27 September 2021): DOI: 10.1080/07350015.2021.1970575.
2. Tan, Zhiqiang
Marginal and Nested Structural Models Using Instrumental Variables
Journal of the American Statistical Association 105,489 (March 2010): 157-169.
Also: http://pubs.amstat.org/doi/abs/10.1198/jasa.2009.tm08299
Cohort(s): Young Men
Publisher: American Statistical Association
Keyword(s): Educational Returns; Modeling; Propensity Scores; Variables, Instrumental

The objective of many scientific studies is to evaluate the effect of a treatment on an outcome of interest ceteris paribus. Instrumental variables (IVs) serve as an experimental handle, independent of potential outcomes and potential treatment status and affecting potential outcomes only through potential treatment status. We propose marginal and nested structural models using IVs, in the spirit of marginal and nested structural models under no unmeasured confounding. A marginal structural IV model parameterizes the expectations of two potential outcomes under an active treatment and the null treatment respectively, for those in a covariate-specific subpopulation who would take the active treatment if the instrument were externally set to each specific level. A nested structural IV model parameterizes the difference between the two expectations after transformed by a link function and hence the average treatment effect on the treated at each instrument level. We develop IV outcome regression, IV propensity score weighting, and doubly robust methods for estimation, in parallel to those for structural models under no unmeasured confounding. The regression method requires correctly specified models for the treatment propensity score and the outcome regression function. The weighting method requires a correctly specified model for the instrument propensity score. The doubly robust estimators depend on the two sets of models and remain consistent if either set of models are correctly specified. We apply our methods to study returns to education using data from the National Longitudinal Survey of Young Men. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Tan, Zhiqiang. "Marginal and Nested Structural Models Using Instrumental Variables." Journal of the American Statistical Association 105,489 (March 2010): 157-169.