Ashley I Naimi, PhD

Assistant Professor, Epidemiology


5131 Public Health, 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, machine learning and human reproduction. Generally, I develop and apply analytic methods to assess the effectiveness of potential policy interventions to reduce the overall burden of adverse pregnancy and childhood outcomes.

My work has focused on the performance, implementation and interpretation of techniques for causal mediation analysis in social epidemiology.  More recently, I have focused on (i) developing, evaluating and applying methods for the analysis complex longitudinal data from randomized trials to estimate per protocol effects of aspirin on pregnancy outcomes (NIH R01 HD093602); (ii) using machine learning and causal inference techniques to inform rigorous national guidelines on dietary patterns that promote healthy pregnancy outcomes (NIH R01 HD102313 with Lisa Bodnar); and (iii) evaluating the performance of various machine learning implementations for the estimation of causal effects, and for predicting reproductive health outcomes.  I am currently the instructor for an advanced epidemiologic methods course focused on causal inference and machine learning.


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

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


2019 | EPIDEM 2187 - Epidemiological Methods 2



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, Mishler AE, Kennedy EH. Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms. American Journal of Epidemiology. 2020 Apr. submitted.


2. Bodnar LM, Cartus AR, Kirkpatrick SI, Himes KP, Kennedy EH, Simhan HN, Grobman WA, Duffy JY, Silver RM, Parry S, Naimi AI. Machine Learning as a Strategy to Account for Dietary Synergy: An Illustration Based on Dietary Intake and Adverse Pregnancy Outcomes. American Journal of Clinical Nutrition. 2020 Jun 1; 111(6):1235-43. PMID: 32108865.


3. Naimi AI, Balzer LB. Stacked generalization: an introduction to super learning. European Journal of Epidemiology. 2018 May; 33(5):459-464. PMID: 29637384.


4. Naimi AI, Platt RW, Larkin JC. Machine Learning for Fetal Growth Prediction. Epidemiology. 2018 Mar; 29(2):290-298. PMID: 29199998.


5. Naimi AI. On wagging tales about causal inference. International Journal of Epidemiology. 2017 Aug 1; 46(4):1340-1342. PMID: 28575465.


6. Naimi AI, Cole SR. Kennedy EH. An introduction to g methods. International Journal of Epidemiology. 2017 Apr 1; 46(2):756-762. PMID: 28039382.


7. Naimi AI, Schnitzer ME, Moodie EE, Bodnar LM. Mediation Analysis for Health Disparities Research. American Journal of Epidemiology. 2016 Aug 15; 184(4):315-24. PMID: 27489089.


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


9. Naimi AI. The Counterfactual Implications of Fundamental Cause Theory. Current Epidemiology Reports. 2016 Mar; 3(1):92-97.doi: 10.1007/s40471-016-0067-7.

Ashley I Naimi