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 provider profiling and personalized medicine.
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 are at the intersection of statistics, machine learning, and causal inference. Some specific topics I have worked on include
- Stochastic and dynamic interventions for observational causal inference (here and here)
- Sensitivity analyses for observational causal inference (here)
- Heterogeneous effect estimation (for incremental effects)
- Nonparametric doubly robust minimax estimation (here)
During my PhD, I also gained invaluable teaching experience. Twice, I instructed CMU’s Sophomore-level Introduction to Statistical Inference course. Additionally, I had the opportunity to serve as a teaching assistant for several courses related to causal inference and other areas of statistics.
I completed my undergraduate education at Swarthmore College, where I earned a Bachelor’s degree in Mathematics and Economics in 2016. Before starting my PhD, I worked for three years as a Research Analyst and later as a Senior Research Analyst at the Brattle Group. There, I helped analyze data and build statistical models for legal, regulatory, and policy issues. In particular, I learned a huge amount from Charlie Gibbons, who inspired me to pursue a career in econometrics/statistics/ML.
Connect
- Email: hadera01@nyu.edu
- Github: alecmcclean
Papers
-
Calibrated sensitivity models
A. McClean, Z. Branson, and E.H. Kennedy
arxiv -
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
A. McClean, S. Balakrishnan, E.H. Kennedy, and L. Wasserman
Winner of Tom Ten Have award at ACIC 2023, arxiv -
Nonparametric Estimation of Conditional Incremental Effects
A. McClean, Z. Branson, and E. H. Kennedy
Journal of Causal Inference, 2024, journal, arxiv -
Incremental causal effects: an introduction and review
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
L. Jacobs, A. McClean, Z. Branson, E. H. Kennedy, and A. Fixler
Journal of Quantitative Criminology, 2023, journal, arxiv
Google Scholar and CV (current as of June 2024)
Software
Contributor to npcausal package
News
(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
- 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, Bernoulli, Biometrika, and the Review of Economics and Statistics
- CMU Statistics Student Activities Committee representative
- Pittsburgh ASA CMU student representative