Workshop: Causal Machine Learning for Real-World Impact

Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design

Mayleen Cortez · Matthew Eichhorn · Christina Yu


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.

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