# Improving Causal Inference Tools for Addiction Researchers

> **NIH NIH R01** · RAND CORPORATION · 2020 · $767,806

## Abstract

Project Summary
Finding truly effective treatment for addiction and substance abuse requires not only knowing that a treatment
works on average for a population, but also knowing the mechanisms or pathways through which a treatment
works and for whom treatment is most effective. Methodologists have been developing new statistical
techniques that rely on fewer assumptions and, as such, are more robust at providing accurate answers than
ever before. However, it can be difficult for many applied researchers to understand how to implement these
new methodologies and few such methods have been implemented in easy-to-use software. This application
requests support for a three-year R01 study to improve the use of robust causal inference methods for
understanding mechanisms and moderators of treatment effectiveness in the field of addiction. We propose to
do so by enhancing existing methods, creating software to implement those methods, and embarking upon a
strategic outreach campaign to facilitate researchers’ use of the new methods and software. Over the past
decade, our team has successfully worked to encourage broader use of propensity score methods for causal
research in the field of addiction by (1) creating and enhancing a statistical software package called TWANG or
Toolkit for Weighting and Analysis of Nonequivalent Groups, which is available in R, SAS, and Stata, (2)
contributing more than 30 methodological articles devoted to implementing propensity score methods, and (3)
conducting over 20 short courses and workshops. Analytic tools such as TWANG which allow researchers to
assess the comparability of groups receiving different treatments and estimate the causal impact of substance
abuse treatment programs using observational data are increasingly valuable as funding for large scale
randomized studies is dwindling. Our team plans to engage in a series of methodological and software
developments that will help improve the next generation of addiction health services research. We aim to
create 20 additional software commands and tutorials that will provide researchers with tools and training for
studying how and for whom treatments work, studying the impact of treatment dose, and assessing the
sensitivity of their estimates to uncontrolled factors. To enhance the utility of our tools, we will develop them in
the R and Stata and in a new computing environment which analyst access through a web browser and is menu
driven. In addition to methodological innovations, the proposed project will include several outreach efforts,
such as short courses, webinars, and peer-review manuscripts to promote best practices among researchers
applying the newly developed tools. Through these efforts, this project aims to not only develop new methods,
tools and software, but also to improve the statistical practices of addiction researchers, greatly strengthening
the scientific information upon which decisions are made to improve care in our country and directly me...

## Key facts

- **NIH application ID:** 9948687
- **Project number:** 5R01DA045049-03
- **Recipient organization:** RAND CORPORATION
- **Principal Investigator:** Beth Ann Griffin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $767,806
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9948687

## Citation

> US National Institutes of Health, RePORTER application 9948687, Improving Causal Inference Tools for Addiction Researchers (5R01DA045049-03). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/9948687. Licensed CC0.

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