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Causal Inference

The aim of causal inference research is to identify the impact of exposure to a particular treatment or program. Much of the Methodology Center's work on causal inference focuses on using propensity scores to determine causality in observational studies. This work allows scientists to evaluate health interventions more accurately and will lead to more effective and efficient treatment and prevention of health and social problems.



Introductory Example: How an HIV-Prevention Intervention Works

How an HIV-Prevention Intervention Works

The Reducing Risky Relationships HIV (RRR-HIV) intervention was designed to decrease incorrect and dangerous thoughts about relationships in order to reduce risky sexual behavior among women being released from prison. Results showed that women who participated in the program engaged in less unprotected sex.


For future interventions, it is important to determine why this happened. Was it because, as hypothesized, the intervention changed their beliefs about relationships? Was it because the intervention reduced their substance use? Or was it because they bonded with other participants in the intervention? With multiple factors at work, determining the mechanism through which the intervention reduced unprotected sex can be difficult. Using a causal mediation model, scientists can answer these types of questions.

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Donna Coffman

Lead researcher:

Donna Coffman 


Other researchers:
Daniel Almirall

Stephanie Lanza

Susan Murphy

Megan Schuler

Center Collaborations


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Our research on causal inference is currently supported by the National Institute on Drug Abuse (NIDA) grant P50-DA10075, National Institute of Environmental Health Sciences grant K01-ES025437, and by the Eunice Kennedy Shriver National Institute of Child Health and Development grant R03-HD079711.


Significant support has been provided by NIDA grant 1R03DA026543-01 and National Institute of Diabetes and Digestive and Kidney Diseases grant R21DK082858. 

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