Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations

TL;DR

This paper introduces a flexible, optimization-based method for sensitivity analysis of causal effects in linear models, accounting for unmeasured confounding using partial correlations and bootstrap confidence intervals.

Contribution

It develops a novel approach that formulates sensitivity analysis as a constrained stochastic optimization problem using partial correlations, with bootstrap-based confidence guarantees.

Findings

Bias of estimators can be expressed via partial correlations.

Proposed bootstrap method provides valid confidence intervals.

Method applied to real data on education and earnings.

Abstract

Causal inference necessarily relies upon untestable assumptions; hence, it is crucial to assess the robustness of obtained results to violations of identification assumptions. However, such sensitivity analysis is only occasionally undertaken in practice, as many existing methods require analytically tractable solutions and their results are often difficult to interpret. We take a more flexible approach to sensitivity analysis and view it as a constrained stochastic optimization problem. This work focuses on sensitivity analysis for a linear causal effect when an unmeasured confounder and a potential instrument are present. We show how the bias of the OLS and TSLS estimands can be expressed in terms of partial correlations. Leveraging the algebraic rules that relate different partial correlations, practitioners can specify intuitive sensitivity models which bound the bias. We further show that the heuristic "plug-in" sensitivity interval may not have any confidence guarantees; instead, we propose a bootstrap approach to construct sensitivity intervals which performs well in numerical simulations. We illustrate the proposed methods with a real study on the causal effect of education on earnings and provide user-friendly visualization tools.