2018/09/02 by Alexander Franks, Franks, Alexander, Alexander D’Amour +3 · 3 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1809.00399
openalex publication_date 2018/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A fundamental challenge in observational causal inference is that assumptions\nabout unconfoundedness are not testable from data. Assessing sensitivity to\nsuch assumptions is therefore important in practice. Unfortunately, some\nexisting sensitivity analysis approaches inadvertently impose restrictions that\nare at odds with modern causal inference methods, which emphasize flexible\nmodels for observed data. To address this issue, we propose a framework that\nallows (1) flexible models for the observed data and (2) clean separation of\nthe identified and unidentified parts of the sensitivity model. Our framework\nextends an approach from the missing data literature, known as Tukey's\nfactorization, to the causal inference setting. Under this factorization, we\ncan represent the distributions of unobserved potential outcomes in terms of\nunidentified selection functions that posit an unidentified relationship\nbetween the treatment assignment indicator and the observed potential outcomes.\nThe sensitivity parameters in this framework are easily interpreted, and we\nprovide heuristics for calibrating these parameters against observable\nquantities. We demonstrate the flexibility of this approach in two examples,\nwhere we estimate both average treatment effects and quantile treatment effects\nusing Bayesian nonparametric models for the observed data.\n