about

I’m a PhD candidate in Statistics at the University of Wisconsin-Madison, where I am co-advised by Keith Levin and Karl Rohe. I study spectral methods for network analysis, causal inference, and causal inference on networks. You might know me from my work on broom, a popular open-source R package in the tidyverse.

I’m on the job market; here’s my academic cv. I keep Google Scholar updated.

Professional headshot of Alex

working papers

  1. Peer effects in the linear-in-means model may be inestimable even when identified. Alex Hayes and Keith Levin. arXiv. October 14, 2024.

  2. Estimating network-mediated causal effects via principal components network regression. Alex Hayes, Mark M. Fredrickson, and Keith Levin. Journal of Machine Learning Research (accepted with minor revisions). September 3, 2024. code

publications

  1. Co-factor analysis of citation networks. Alex Hayes and Karl Rohe. Journal of Computational and Graphical Statistics. 2024. post-print, arXiv, replication package, code

  2. Welcome to the tidyverse. Hadley Wickham, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, Alex Hayes, Lionel Henry, Jim Hester, Max Kuhn, Thomas Lin Pedersen, Evan Miller, Kirill Müller, David Robinson, Dana Paige Seidel, Vitalie Spinu, Kohske Takahashi, Davis Vaughan, Claus Wilke, Kara Woo, Hiroaki Yutani. Journal of Open Source Software. 2019. website

recent and upcoming presentations

Estimating peer influence: limitations of linear-in-means models
2024-11-12 @ 2:35 pm, American Family Funding Initiative Networking Meeting
poster

Estimating peer influence: limitations of linear-in-means models
2024-11-22 @ 5 pm, Wisconsin ASA Chapter Meeting
poster

Material for older presentations is available on my talks page.

In a hobbyist capacity, I also blog about statistics, programming, and data. Some posts I’m particularly proud of:

These days I’m enjoying the slow resurgence of academic Twitter on Bluesky. You can find me there at @alexpghayes.com.