Chris Hays

Hello! I am a 4th-year PhD student in Social and Engineering Systems and Statistics at the MIT Institute for Data, Systems, and Society.

Broadly, I am interested in studying the effects of machine learning systems and digital platforms on society, and I am very fortunate to be advised by Manish Raghavan. My PhD is supported by an NDSEG fellowship.

In 2020, I graduated from Yale with a bachelor's degree in computer science.

Papers

(α-β = authorship ordered alphabetically; * = equal contributions)

Double Machine Learning for Causal Inference under Shared-State Interference
Chris Hays, Manish Raghavan.
ICML 2025.

Inducing Efficient and Equitable Professional Networks through Link Recommendations
Cynthia Dwork, Chris Hays, Nicole Immorlica, Lunjia Hu, Juan C. Perdomo.^α-β
Preprint.

From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs
Cynthia Dwork, Chris Hays, Nicole Immorlica, Juan C. Perdomo, Pranay Tankala.^α-β
COLT 2025.

Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity
Cynthia Dwork, Chris Hays, Jon Kleinberg, Manish Raghavan.^α-β
EC 2024; FORC 2024 (non-archival).

Content Moderation and the Formation of Online Communities: A Theoretical Framework
Cynthia Dwork, Chris Hays, Jon Kleinberg, Manish Raghavan.^α-β
WWW 2024 (oral presentation); FORC 2024 (non-archival).

Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
Chris Hays*, Zachary Schutzman*, Manish Raghavan, Erin Walk, Philipp Zimmer.
WWW 2023. Best paper award.

The Effect of the Rooney Rule on Implicit Bias in the Long Term
L. Elisa Celis, Chris Hays, Anay Mehrotra, Nisheeth K. Vishnoi.^α-β
FAccT 2021.