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.

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Connect

• Email: alec@stat.cmu.edu
• Github: alecmcclean

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Papers

• Nonparametric Estimation of Conditional Incremental Effects
  A. McClean, Z. Branson, and E. H. Kennedy
  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 Houselessness, Behavioral Health Problems, and Recidivism
  L. Jacobs, A. McClean, Z. Branson, E. H. Kennedy, and A. Fixler
  arxiv

Google Scholar and CV (current as of September 2023)

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News

My poster on double cross-fit doubly robust estimators won the Ten Have award at ACIC 2023. I plan to upload the paper to Arxiv very soon!

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Teaching

As Course Instructor

• Introduction to Statistical Inference (Summer 2022)

As Teaching Assistant

• Intermediate Statistics 36-705 (Fall 2023)
• 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 - served as Head TA)
• Methods for Statistics and Data Science (Summer 2021)
• Advanced Methods for Data Analysis (Spring 2021 - served as Head TA)
• Modern Regression (Fall 2019)

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Service

• CMU Statistics Student Activites Committee representative
• Pittsburgh ASA CMU student representative