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- An Introduction to Causal Inference by Pearl, Judea
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- Meta-Analysis of Observational Studies with Unmeasured Confounders by McCandless, Lawrence C.
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
- A Refreshing Account of Principal Stratification by Mealli, Fabrizia and Mattei, Alessandra
Selective Ignorability Assumptions in Causal Inference
1University of Pennsylvania School of Medicine
2University of Pennsylvania School of Medicine
3University of Pennsylvania School of Medicine
Citation Information: The International Journal of Biostatistics. Volume 6, Issue 2, Pages –, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1199, March 2010
- Published Online:
Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually, largely because they justify the use of available statistical methods and not because they are truly believed. It will often be the case that it is plausible that conditional independence holds at least approximately for a subset but not all of the experience giving rise to one's data. Such selective ignorability assumptions may be used to derive valid causal inferences in conjunction with structural nested models. In this paper, we outline selective ignorability assumptions mathematically and sketch how they may be used along with otherwise standard G-estimation or likelihood-based methods to obtain inference on structural nested models. We also consider use of these assumptions in the presence of selective measurement error or missing data when the missingness is not at random. We motivate and illustrate our development by considering an analysis of an observational database to estimate the effect of erythropoietin use on mortality among hemodialysis patients.