Alec McClean

I am a Postdoctoral Fellow in the Division of Biostatistics in the NYU Grossman School of Medicine, where I am fortunate to work with Iván Díaz and Wenbo Wu. We are developing theory and methods at the intersection of machine learning and causal inference, and applying them to problems in healthcare. Time permitting, I also help the Tech & Society lab team assess the evidence of the causal effects of social media use on mental health. Before my postdoc, I did my PhD in Statistics at Carnegie Mellon University, where I was advised by Zach Branson and Edward Kennedy. Additionally, I had the pleasure of working with Siva Balakrishnan and Larry Wasserman.

My primary research interests lie at the intersection of statistics, machine learning, and causal inference. In particular, I develop nonparametric statistical methods that leverage machine learning to estimate causal effects from complex observational data. Much of my work focuses on ensuring these methods remain robust to violations of key causal assumptions, such as positivity and unconfoundedness. I have received the Ten Have award for exceptional research in causal inference.

Connect

Papers

Google Scholar and CV (current as of July 2025)

Software

Contributor to npcausal package

News

(Jul ‘25) Our new paper on counterfactual longitudinal propensity score weighting is on arxiv

(June ‘25) Our new paper on flip interventions for weighting and trimmed with longitudinal data is on arxiv

(May ‘25) presenting our new (and ongoing) work on weighting with longitudinal data at ACIC! slides preliminary draft

(Apr ‘25) I presented our new (and ongoing) work on trimming with longitudinal data at EuroCIM! preliminary draft slides

(Mar ‘25) I recently presented our fair comparisons work at ENAR. slides

(Oct ‘24) We have a new paper on arxiv on fair comparisons of causal parameters; more details on Bluesky

(Jul ‘24) I started as a Postdoctoral fellow at NYU working with Iván Díaz and Wenbo Wu!

(May ‘24) I presented our ongoing work on calibrated sensitivity models at ACIC 2024 (slides). A working paper is up on arxiv

(Mar ‘24) We have a new paper on arxiv on double cross-fit doubly robust estimators!

Teaching

As Course Instructor

As Teaching Assistant

Service