Flexible Imputation of Missing Data, 2nd ed.
- Shu Yang
- Computer ScienceJournal of the American Statistical Association
- 3 July 2019
The main objective of the book is to provide a tool kit for practitioners to execute multiple imputation, and it avoids too much mathematical and technical details and uses graphical tools and visual displays to aid understanding.
Propensity score matching and subclassification in observational studies with multi‐level treatments
It is shown, using the concept of weak unconfoundedness and the notion of the generalized propensity score, that adjusting for a scalar function of the pret treatment variables removes all biases associated with observed pretreatment variables.
Integrative analysis of randomized clinical trials with real world evidence studies
The proposed methods to estimate the effect of adjuvant chemotherapy in early-stage resected non-small-cell lung cancer integrating data from a RCT and a sample from the National Cancer Database are applied.
A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
- B. Reich, Shu Yang, Yawen Guan, A. Giffin, M. J. Miller, A. Rappold
- Computer ScienceInternational Statistical Review
- 6 July 2020
The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID‐19 mortality rate.
Propensity Score Weighting for Causal Inference with Clustered Data
- Shu Yang
- EconomicsJournal of Causal Inference
- 17 March 2017
Abstract Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering,…
Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores
&NA; Causal inference with observational studies often relies on the assumptions of unconfoundedness and overlap of covariate distributions in different treatment groups. The overlap assumption is…
Doubly robust inference when combining probability and non‐probability samples with high dimensional data
- Shu Yang, Jae Kwang Kim, Rui Song
- MathematicsJournal of The Royal Statistical Society Series B…
- 12 March 2019
We consider integrating a non‐probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two‐step…
Fractional Imputation in Survey Sampling: A Comparative Review
The empirical performance of FI is demonstrated with respect to multiple imputation using a pseudo finite population generated from a sample from the Monthly Retail Trade Survey conducted by the US Census Bureau.
Improving trial generalizability using observational studies.
A calibration weighting estimator is proposed that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability and exploiting semiparametric efficiency theory.
A spectral adjustment for spatial confounding
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence…