Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap
By Qingyuan Zhao, Dylan S. Small, Bhaswar B. Bhattacharya. Journal of The Royal Statistical Society Series B-statistical Methodology, 2017
169 citations recorded by the source corpus.
To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the ‘missingness at random’ and ‘no unmeasured confounder’ assumptions. However, these assumptions are unverifiable by using empirical data and pose serious threats to the validity of the qualitative conclusions of statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated to a certain degree. We consider a marginal sensitivity model which is a natural extension of Rosenbaum's sensitivity model that is widely used for matched observational studies. We aim to construct confidence intervals based on inverse probability weighting estimators, such that asymptotically the intervals have at least nominal coverage of the estimand whenever the data‐generating distribution is in the collection of marginal sensitivity models. We use a percentile bootstrap and a generalized minimax–maximin inequality to transform this intractable problem into a linear fractional programming problem, which can be solved very efficiently. We illustrate our method by using a real data set to estimate the causal effect of fish consumption on blood mercury level.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/8863f6eecefb48eadf5b3f8281051e3c65433f19
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
Citations in the corpus
- Confounding-Robust Policy Improvement (2018, methodology): “Zhao et al. (2017) consider bounds on the Hájek estimator, but impose a parametric model on the treatment assignment probability.”
- 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).”
- Collider bias undermines our understanding of COVID-19 disease risk and severity (2020, background): “Zhao et al (2017) developed a sensitivity analysis for the degree to which estimated probability weights differ from the true probability weights due to misspecification (53).”