# Using Causal Inference and Machine Learning Methods to Predict Cognitive Behavioral Treatment Response

> **NIH NIH R21** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $207,977

## Abstract

PROJECT SUMMARY/ABSTRACT
Many patients with anxiety and fear disorders (AFDs) report minimal benefits when treated with an evidence-
based psychotherapy (e.g., cognitive-behavioral therapy [CBT]) or pharmacotherapy (e.g., antidepressants;
benzodiazepines). Conversely, some AFD patients are likely to benefit from virtually any treatment (e.g.,
supportive therapy). Using data collected in randomized controlled trials (RCTs), there has been limited
progress determining how to use pre-treatment characteristics to match AFD patients to the treatment that is
most likely to provide benefit. As a result, NIMH has forwarded Strategic Objectives focused on identifying
treatment moderators and developing tools that predict differential treatment response. The broad goal of this
proposed secondary data analysis is to apply causal inference and machine learning methods to prospective
observational data to predict differential treatment response among patients with AFDs. The sample (n =
1,528) is from a longstanding NIMH-funded study of AFD patients who received: (a) CBT with concurrent
pharmacotherapy, (b) CBT without pharmacotherapy, or (c) treatment as usual (TAU). Targeted maximum
likelihood estimation (a causal inference method) and super learning (an ensemble machine learning method)
will be used to accomplish the proposed Aims. Aim 1 will estimate the (overall) average effects of the three
treatment types. Aim 2 will estimate “optimal treatment rules” to determine if differential treatment response
can be meaningfully predicted based on a patient's multidimensional profile of pre-treatment symptoms. Aim 3
will estimate “optimal treatment rules” to determine if differential treatment response can be meaningfully
predicted using all available pre-treatment covariates. The proposed study is highly innovative and could
significantly impact the growing literature focused on predicting differential treatment effects and personalizing
treatment for patients with AFDs. Although treatment effects estimates obtained using observational data and
causal inference methods (i.e., adjusted for nonrandom treatment selection) are similar to those estimated in
RCTs, this would be the first study to apply such methods to AFD patient data. This study will also be the first
to use ensemble machine learning to develop composite moderators for AFDs (i.e., optimal treatment rules). In
comparison, prior attempts to develop composite moderators have relied on less flexible model-building
procedures prone to overfitting and unable to capture complex predictor-outcome associations (e.g.,
interactions among predictors; nonlinear associations). In achieving the proposed Aims, the current study
would be a catalyst for future research using causal inference and machine learning to study predictors of
differential treatment response among AFD patients. Results will be used to justify future research aimed at
expanding and validating the models in larger observational samples and pragma...

## Key facts

- **NIH application ID:** 9912204
- **Project number:** 5R21MH119492-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Anthony Joseph Rosellini
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $207,977
- **Award type:** 5
- **Project period:** 2019-04-09 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9912204

## Citation

> US National Institutes of Health, RePORTER application 9912204, Using Causal Inference and Machine Learning Methods to Predict Cognitive Behavioral Treatment Response (5R21MH119492-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9912204. Licensed CC0.

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