# Universal Sensitivity Analysis for Unmeasured Confounding in Drug-Related Public Policy Evaluation

> **NIH NIH R21** · NEW YORK UNIVERSITY · 2024 · $216,211

## Abstract

Project Summary
Unmeasured confounding is a major source of bias in causal inference for drug-related public policy
evaluation, and a sensitivity analysis is typically needed to examine how sensitive a related causal conclusion
is to unmeasured confounding. Existing sensitivity analysis methods are underdeveloped in drug-related policy
evaluation and can severely harm the evidence for (or against) causal claims. For example, in matched
observational studies, one of the most widely used causal inference methods in policy research, existing
sensitivity analysis methods typically focus on the case when the treatment is binary or there is a single
outcome; also, they often ignore possible subgroup-specific effects. However, many drug-related policy
measures are non-binary (e.g., ordinal or continuous), such as alcohol or tobacco tax rates, minimum legal
purchase ages, alcohol policy scores, tobacco control index, and mobility scores. Also, drug-related policies
are often evaluated using several outcomes, either those related to different types of drug use, or those related
to different aspects of society such as health, justice, and economics. Finally, due to existing disparities in
drug-related outcomes, there is an intense focus on accurately measuring the effects of drug-related policies
among subgroups defined by race and socioeconomic status. The broad objective of this project is to develop
a universal sensitivity analysis framework for unmeasured confounding in matched observational studies that
can work with binary or non-binary treatments, single or multiple outcomes, and overall or subgroup-specific
effects. There are three specific aims. Aim 1 will develop a universal sensitivity analysis framework for matched
observational studies with general (binary or non-binary) treatments. Aim 2 will further develop the sensitivity
analysis for multiple outcomes and subgroup-specific effects. Aim 3 will illustrate the proposed sensitivity
analysis by studying the causal influences of mobility policies, such as social distancing policies and
transportation policies where the measures (e.g., mobility scores) are continuous in nature, on drug-related
outcomes (e.g., drug overdose deaths, tobacco use, and excessive drinking). We will evaluate the effect on the
overall population and those among different racial groups. Aim 3 will also develop a publicly available and
user-friendly R package to implement our universal sensitivity analysis framework.

## Key facts

- **NIH application ID:** 10868072
- **Project number:** 1R21DA060433-01
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Siyu Heng
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $216,211
- **Award type:** 1
- **Project period:** 2024-04-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10868072

## Citation

> US National Institutes of Health, RePORTER application 10868072, Universal Sensitivity Analysis for Unmeasured Confounding in Drug-Related Public Policy Evaluation (1R21DA060433-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10868072. Licensed CC0.

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