# Selective Ignorability Assumptions in Causal Inference

TL;DR: This paper outlines selective ignorability assumptions mathematically and sketches how they may be used along with otherwise standard G-estimation or likelihood-based methods to obtain inference on structural nested models to derive valid causal inferences.

Abstract: 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.

## Summary (1 min read)

### Summary

- 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.
- The authors 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.
- The authors also consider use of these assumptions in the presence of selective measurement error or missing data when the missingness is not at random.

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##### Citations

378 citations

### Cites background from "Selective Ignorability Assumptions ..."

...Such conditions are extremely difficult to interpret (unaided by graphical tools) and “are usually made casually, largely because they justify the use of available statistical methods and not because they are truly believed” (Joffe et al. 2010)....

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238 citations

235 citations

### Cites background from "Selective Ignorability Assumptions ..."

...The verification of such independencies, often called “strong ignorability,” “conditional ignorability,” or “sequential ignorability,” presents a formidable judgmental task to most researchers if unaided by structural models (Joffe et al., 2010)....

[...]

93 citations

77 citations