Distributionally enhanced marginal sensitivity model and bounds

TL;DR

This paper introduces deMSM, a new sensitivity analysis model that tightens bounds on causal effects by adding a second distribution shift constraint, improving interpretability and applicability in unmeasured confounding scenarios.

Contribution

The paper proposes deMSM, an enhanced marginal sensitivity model with dual constraints, providing sharper and more interpretable bounds for causal inference under unmeasured confounding.

Findings

deMSM yields tighter bounds than traditional MSM.

The bounds are symmetric and easier to interpret.

Comparison shows deMSM improves over existing models.

Abstract

For sensitivity analysis against unmeasured confounding, we build on the marginal sensitivity model (MSM) and propose a new model, deMSM, by incorporating a second constraint on the shift of potential outcome distributions caused by unmeasured confounders in addition to the constraint on the shift of treatment probabilities. We show that deMSM leads to interpretable sharp bounds of common causal parameters and tightens the corresponding MSM bounds. Moreover, the sharp bounds are symmetric in the two deMSM constraints, which facilitates practical applications. Lastly, we compare deMSM with other MSM-related models in both model constraints and sharp bounds, and reveal new interpretations for later models.