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Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design
Mayleen Cortez · Matthew Eichhorn · Christina Yu

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in Causal Machine Learning for Real-World Impact »
Event URL: https://openreview.net/forum?id=xsHg4aokztS »

Network interference, where the outcome of an individual is affected by the treatment of others in their social network, is pervasive in real-world settings. However, it poses a challenge to estimating causal effects. We consider the task of estimating the total treatment effect (TTE), or the difference between the average outcomes of the population when everyone is treated versus when no one is, under network interference. Under a non-uniform Bernoulli randomized design, we utilize knowledge of the network structure to provide an unbiased estimator for the TTE when network interference effects are constrained to low-order interactions among neighbors of an individual. We make no assumptions on the graph other than bounded degree, allowing for well-connected networks that may not be easily clustered. We derive a bound on the variance of our estimator and show in simulated experiments that it performs well compared with standard TTE estimators.

Author Information

Mayleen Cortez (Cornell University)

Mayleen Cortez-Rodriguez is a third-year Applied Mathematics Ph.D. student at Cornell University. She graduated from California State University, Channel Islands in May 2020 with a B.S. in Mathematics and a minor in Computer Science. She is a National Science Foundation Graduate Research Fellowship recipient. Past research interests include mathematical modeling and reinforcement learning with applications to biology and public health. She is currently working with Dr. Christina Yu on problems in causal inference under interference.

Matthew Eichhorn (Cornell University)

I am a fourth-year PhD student in the Center for Applied Mathematics at Cornell University, where I am advised by Siddhartha Banerjee. My research focuses on the design of algorithms for combinatorial problems, particularly with applications to game theory. In addition, I have a passion for teaching and curricular development. I have helped to develop undergraduate course materials for the Cornell Math Department's Active Learning Initiative. Beyond academia, I am an avid baker and a devoted fan of the Buffalo Bills.

Christina Yu (Cornell University)

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