In defense of MAR over latent ignorability (or latent MAR) for outcome missingness in studying principal causal effects: a causal graph view

TL;DR

This paper argues that the MAR assumption is more plausible and easier to satisfy than the LMAR assumption in principal stratification analysis, using causal graph representations to clarify conditions for outcome missingness.

Contribution

The paper introduces a causal graph framework to compare MAR and LMAR assumptions, demonstrating MAR's advantages and clarifying conditions for its validity in principal causal effect studies.

Findings

LMAR is more difficult to satisfy than MAR.

Conditioning on principal stratum offers no benefit over observed variables for missingness.

MAR assumption can be justified under specific causal structures and auxiliary variables.

Abstract

This paper concerns outcome missingness in principal stratification analysis. We revisit a common assumption known as latent ignorability or latent missing-at-random (LMAR), often considered a relaxation of missing-at-random (MAR). LMAR posits that the outcome is independent of its missingness if one conditions on principal stratum (which is partially unobservable) in addition to observed variables. The literature has focused on methods assuming LMAR (usually supplemented with a more specific assumption about the missingness), without considering the theoretical plausibility and necessity of LMAR. In this paper, we devise a way to represent principal stratum in causal graphs, and use causal graphs to examine this assumption. We find that LMAR is harder to satisfy than MAR, and for the purpose of breaking the dependence between the outcome and its missingness, no benefit is gained from conditioning on principal stratum on top of conditioning on observed variables. This finding has an important implication: MAR should be preferred over LMAR. This is convenient because MAR is easier to handle and (unlike LMAR) if MAR is assumed no additional assumption is needed. We thus turn to focus on the plausibility of MAR and its implications, with a view to facilitate appropriate use of this assumption. We clarify conditions on the causal structure and on auxiliary variables (if available) that need to hold for MAR to hold, and we use MAR to recover effect identification under two dominant identification assumptions (exclusion restriction and principal ignorability). We briefly comment on cases where MAR does not hold. In terms of broader connections, most of the MAR findings are also relevant to classic instrumental variable analysis that targets the local average treatment effect; and the LMAR finding suggests general caution with assumptions that condition on principal stratum.