Flexible Sensitivity Analysis for Observational Studies Without Observable Implications

A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restriction...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2020-12, Vol.115 (532), p.1730-1746
Main Authors: Franks, AlexanderM, D'Amour, Alexander, Feller, Avi
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian inference
Factorization
Flexibility
Heuristic
Inference
Latent confounder
Mathematical models
Missing data
Observational studies
Parameter sensitivity
Regression analysis
Sensitivity analysis
Statistical methods
Statistics
Tukey's factorization
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Summary:A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restrictions that are at odds with modern causal inference methods, which emphasize flexible models for observed data. To address this issue, we propose a framework that allows (1) flexible models for the observed data and (2) clean separation of the identified and unidentified parts of the sensitivity model. Our framework extends an approach from the missing data literature, known as Tukey's factorization, to the causal inference setting. Under this factorization, we can represent the distributions of unobserved potential outcomes in terms of unidentified selection functions that posit a relationship between treatment assignment and unobserved potential outcomes. The sensitivity parameters in this framework are easily interpreted, and we provide heuristics for calibrating these parameters against observable quantities. We demonstrate the flexibility of this approach in two examples, where we estimate both average treatment effects and quantile treatment effects using Bayesian nonparametric models for the observed data.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2019.1604369