I am a Research Scientist at Ataraxis AI, where I build cutting-edge AI models for cancer prognosis and treatment selection.
Before joining Ataraxis AI, I was a Postdoctoral Fellow in the Division of Biostatistics at NYU Grossman School of Medicine, where I worked with Iván Díaz and Wenbo Wu on theory and methods at the intersection of statistics, machine learning, and causal inference, with applications in healthcare. I also collaborated with the Tech & Society Lab to assess the causal effects of social media use on mental health. I received my PhD in Statistics from Carnegie Mellon University, where I was advised by Zach Branson and Edward Kennedy, and worked closely with Siva Balakrishnan and Larry Wasserman.
My research develops nonparametric statistical methods that leverage machine learning to estimate causal effects from complex observational and longitudinal data, with an emphasis on robustness to assumption violations and statistical efficiency in high-dimensional settings. This work has been recognized with the Tom Ten Have Award for exceptional research in causal inference. Recently, I have focused on longitudinal causal inference, developing new interventions and efficient sequential double machine learning estimators that address positivity violations.
Connect
- Linkedin: alec-mcclean
- Bluesky: alecmcclean.bsky.social
- Email: amcclean@alumni.cmu.edu
- Github: alecmcclean
Selected papers
Google Scholar and CV (current as of October 2025)
-
Non-overlap average treatment effect bounds Preprint
H. Susmann*, A.McClean*, and I. Díaz
arxiv, bluesky
* Equal contribution -
Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations Preprint
A. McClean and I. Díaz
arxiv, bluesky -
Longitudinal weighted and trimmed treatment effects with flip interventions Under Review
A. McClean, A. Levis, N. Williams, and I. Díaz
arxiv, bluesky, slides -
Comparing causal parameters with many treatments and positivity violations In Press
A. McClean, Y. Li, S. Bae, M. A. McAdams-DeMarco, I. Díaz, and W. Wu
Biometrika, 2026, journal, arxiv, bluesky, linkedin, slides -
Stochastic interventions, sensitivity analysis, and optimal transport Preprint
A. Levis, E. H. Kennedy, A. McClean, S. Balakrishnan, and L. Wasserman
arxiv -
Calibrated sensitivity models In Press
A. McClean, Z. Branson, and E.H. Kennedy
Biometrika, 2026, journal, arxiv, bluesky, slides -
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression In Press
A. McClean, S. Balakrishnan, E.H. Kennedy, and L. Wasserman
Accepted at JRSSB, arxiv, bluesky
Winner of Tom Ten Have award at ACIC 2023, -
Nonparametric Estimation of Conditional Incremental Effects Published
A. McClean, Z. Branson, and E. H. Kennedy
Journal of Causal Inference, 2024, journal, arxiv -
Incremental causal effects: an introduction and review Published
M. Bonvini*, A. McClean*, Z. Branson, and E. H. Kennedy
Handbook of Matching and Weighting in Causal Inference, 2023, arxiv
* Equal contribution -
Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Homelessness, Behavioral Health Problems, and Recidivism Published
L. Jacobs, A. McClean, Z. Branson, E. H. Kennedy, and A. Fixler
Journal of Quantitative Criminology, 2023, journal, arxiv
Software
Contributor to npcausal package
News
- (Mar '26) Our paper on comparing causal parameters with many treatments was accepted at Biometrika!
- (Mar '26) Our paper on double cross-fit doubly robust estimators was accepted at JRSSB!
- (Jan '26) Our paper on calibrated sensitivity models was accepted at Biometrika link
- (Sept '25) Our new paper on non-overlap average treatment effect bounds is on arxiv
- (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 trimming with longitudinal data is on arxiv
- (Oct '24) We have a new paper on arxiv on fair comparisons of causal parameters; more details on Bluesky
- (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
- Introduction to Statistical Inference (Summer 2022 and Spring 2024)
As Teaching Assistant
- Introduction to Statistical Inference (Spring 2024 - Head TA)
- Intermediate Statistics 36-705 (Fall 2023 - Head TA)
- Optum Summer Undergraduate Research Experience (Summer 2023)
- Introduction to Causal Inference 36-318 (Spring 2022 and 2023)
- Foundations and Modern Causal Inference (Fall 2022)
- Statistical Reasoning with R 90-711 (Fall 2020 and 2021 - Head TA)
- Methods for Statistics and Data Science (Summer 2021)
- Advanced Methods for Data Analysis (Spring 2021 - Head TA)
- Modern Regression (Fall 2019)
Service
- Reviewer for ACIC 2024, the American Journal of Epidemiology, the Annals of Statistics, Behavioral Research Methods, Bernoulli, Biometrika, JASA Theory \& Methods, Observational Studies, the Review of Economics and Statistics, and Statistics in Medicine
- CMU Statistics Student Activities Committee representative
- Pittsburgh ASA CMU student representative