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Ashley I Naimi, PhD

Assistant Professor, Epidemiology


5131 Parran Hall, 130 DeSoto Street, Pittsburgh, PA 15261
R-znvy: nfuyrl.anvzv@cvgg.rqh
Primary Phone: 967-179-8629
Fax: 967-179-2842
Web site:

Ellen Mooney, ryz664@cvgg.rqh, 967-179-6808

Personal Statement

My research falls at the crossroads of causal inference, social epidemiology and human reproduction.  Generally, I develop and apply analytic methods to non-experimental data to assess the effectiveness of potential policy interventions to reduce the overall burden of and social disparities in adverse pregnancy and childhood outcomes.

A major focus of my current work is the development, use and interpretation of statistical methods for causal mediation analysis in social epidemiology.  I am adapting a variety of modeling techniques to estimate more realistic intervention effects (stochastic mediation contrasts).  My approach relies heavily on the principles and concepts of causal inference, comparative effectiveness research and implementation science.  The end goal of this research is to develop targeted actionable strategies to reduce racial disparities in preterm birth.


2013 | McGill University, Montreal, QC, Canada | Post-Doctoral Research Fellowship

2012 | University of North Carolina at Chapel Hill, Chapel Hill, NC | PhD



Research Interests

  • Reproductive/Perinatal Epidemiology
  • Social Epidemiology
  • Causal Inference
  • Systems Science
  • Machine Learning

Honors and Awards

Lilienfeld Post-Doctoral Prize Paper, Society for Epidemiologic Research (SER), June 2015

Selected Publications

1. Naimi AI, Larkin JC, Platt RW. Machine Learning for Fetal Growth Prediction. Epidemiol. in press.


2. Naimi AI. On wagging tales about causal inference. Int J Epidemiol. 2017. [Epub ahead of print]. PMID: 28575465.


3. Naimi AI, Cole SR. Kennedy EH. An introduction to g methods. Int J Epidemiol. 2017; 46(2): 756-762. PMID: 28039382.


4. Naimi AI, Schnitzer ME, Moodie EE, Bodnar LM. Mediation Analysis for Health Disparities Research. Am J Epidemiol. 2016; 184(4): 315-24. PMID: 27489089.


5. Naimi AI. Commentary: Integrating Complex Systems Thinking into Epidemiologic Research. Epidemiology. 2016; 27(6): 843-7. PMID: 27488060.


6. Naimi AI. The Counterfactual Implications of Fundamental Cause Theory. Curr Epidemiol Rep. 2016. 3(1): 92-97. doi 10.1007/s40471-016-0067-7.


7. Naimi AI. Invited Commentary: Boundless Science: Putting Natural Direct and Indirect Effects in a Clearer Empirical Context. Am J Epidemiol. 2015. 182(2): 109-14. PMID: 25944884.


8. Naimi AI, Tchetgen Tchetgen EJ. Invited Commentary: Estimating Population Impact in the Presence of Competing Events. Am J Epidemiol. 2015. 181(8): 571-4. PMID: 25816819.

Ashley I Naimi
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