I am a 5th-year PhD student in Statistics at Carnegie Mellon University, where I am advised by Zach Branson and Edward Kennedy. Additionally, I have 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 topics I have worked on include
- Stochastic and dynamic interventions for observational causal inference
- Sensitivity analyses for observational causal inference
- Heterogeneous effect estimation
- Nonparametric doubly robust minimax estimation
During my PhD, I have gained experience as a teaching assistant for several courses related to causal inference and other areas of statistics. I also instructed CMU’s Sophomore-level Introduction to Statistical Inference course.
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.
Connect
- Email: alec@stat.cmu.edu
- Github: alecmcclean
Papers
-
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
Accepted at the Journal of Causal Inference, 2024, 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
Accepted at the Journal of Quantitative Criminology, 2023, arxiv
Google Scholar and CV (current as of March 2024)
Software
Contributor to npcausal package
News
Our paper, Nonparametric Estimation of Conditional Incremental Effects, was accepted at the Journal of Causal Inference
Our paper, Incremental Propensity Score Effects for Criminology, was accepted at the Journal of Quantitative Criminology
My poster on double cross-fit doubly robust estimators won the Ten Have award at ACIC 2023
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
- Referee for Bernoulli and American Journal of Epidemiology
- CMU Statistics Student Activities Committee representative
- Pittsburgh ASA CMU student representative