Sensitivity Analysis of Inverse Probability Weighting Estimators of Causal Effects in Observational Studies with Multivalued Treatments

TL;DR

This paper introduces a new sensitivity analysis framework for estimating causal effects in observational studies with multivalued treatments, addressing the challenge of unmeasured confounding.

Contribution

It proposes a novel, efficient sensitivity analysis method applicable to multivalued treatments, expanding existing causal inference tools.

Findings

Framework performs well in bias reduction and confidence interval coverage.

Simulation results confirm effectiveness with sufficient covariate overlap.

Applied to study on fish consumption and mercury levels.

Abstract

One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Therefore, assessing sensitivity to this assumption's violation is important to obtain valid causal conclusions in observational studies. Although several sensitivity analysis frameworks are available in the casual inference literature, very few of them are applicable to observational studies with multivalued treatments. To address this issue, we propose a sensitivity analysis framework for performing sensitivity analysis in multivalued treatment settings. Within this framework, a general class of additive causal estimands has been proposed. We demonstrate that the estimation of the causal estimands under the proposed sensitivity model can be performed very efficiently. Simulation results show that the proposed framework performs well in terms of bias of the point estimates and coverage of the confidence intervals when there is sufficient overlap in the covariate distributions. We illustrate the application of our proposed method by conducting an observational study that estimates the causal effect of fish consumption on blood mercury levels.