The Causal Inference Methods Research Pillar in the Division of Biostatistics is a dynamic hub where faculty, PhD students, research scientists, and postdoctoral fellows focus on advancing and applying causal inference methodologies for both observational and randomized studies. The group explores a diverse range of cutting-edge topics, including causal inference in complex trials, non-parametric statistical methods, and approaches for analyzing longitudinal and survival data. Research efforts also delve into causal inference with network data, the identification and estimation of heterogeneous treatment effects, and the development of dynamic treatment regimes. Additional areas of focus include mediation analysis and optimal weighting methods, all aimed at enhancing the rigor and applicability of causal inference in diverse study contexts.

Lead Faculty

Samrachana Adhikari, PhD
Associate Professor

Ivan L. Diaz, PhD
Associate Professor

Faculty Members

Judith D. Goldberg , ScD
Professor

Erinn M. Hade, PhD
Associate Professor

Nicholas Illenberger, PhD
Assistant Professor

Matthew Lee, DrPH, MPH
Assistant Professor

Ting-Fang Lee
Research Assistant Professor

Huilin Li, PhD
Professor

Soutrik Mandal, PhD
Assistant Professor

Hyung G. Park, PhD
Assistant Professor

Michele Santacatterina, PhD
Assistant Professor

Yidan Shi, PhD
Assistant Professor

Chan Wang, PhD
Research Assistant Professor

Our Methodology Research

• M Santacatterina. Robust weights that optimally balance confounders for estimating marginal hazard ratios. SMMR, 2023.
• Illenberger, Nicholas A., Dylan S. Small, and Pamela A. Shaw. "Impact of regression to the mean on the synthetic control method: bias and sensitivity analysis." Epidemiology 31.6 (2020): 815-822.
• Adhikari, Samrachana, Sherri Rose, and Sharon-Lise Normand. "Nonparametric Bayesian instrumental variable analysis: Evaluating heterogeneous effects of coronary arterial access site strategies." Journal of the American Statistical Association 115.532 (2020): 1635-1644.
• Rudolph, K. E., N. Williams, and I. Díaz. "Using instrumental variables to address unmeasured confounding in causal mediation analysis." Biometrics 80.1 (2024): ujad037-ujad037.

Our Collaborative Research

• Rudolph, Kara E., Nicholas T. Williams, and Ivan Diaz. "Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders." Biostatistics (2024): kxae012.
• Khatana SAM, Illenberger N, Werner RM, Groeneveld PW, Mitra N. Changes in Supplemental Nutrition Assistance Program Policies and Diabetes Prevalence: Analysis of Behavioral Risk Factor Surveillance System Data From 2004 to 2014. Diabetes Care. 2021 Dec;44(12):2699-2707. doi: 10.2337/dc21-1203. Epub 2021 Oct 4. PMID: 34607835; PMCID: PMC8669531.
• Slaughter JL, Klebanoff MA, Hade EM. Estimating the effect of diuretics and inhaled corticosteroids for evolving bronchopulmonary dysplasia in preterm infants. Paediatr Perinat Epidemiol. 2024 Jan 8. doi: 10.1111/ppe.13038. Epub ahead of print. PMID: 38192005

Contact Us

For more information about the Causal Inference Methods Research Pillar, please contact Samrachana Adhikari, PhD, at Samrachana.Adhikari@NYULangone.org or Ivan L. Diaz, PhD, at Ivan.Diaz@NYULangone.org.