Off-policy causal estimation in networks
Measuring treatment effects when people influence each other's outcomes
When one person's treatment affects their neighbors' outcomes — common in social networks — researchers face a puzzle: how do you estimate what would happen under a different policy than the one that generated your data? This paper solves that puzzle by constructing weights that mathematically transport data from one policy to another, even when interference patterns are misspecified, and provides tools to measure the resulting bias-variance trade-off.
Social media platforms, public health campaigns, and online marketplaces all operate in networked settings where one person's treatment ripples to others. Current methods for estimating causal effects assume people are isolated — a false assumption that leads to wrong answers. This work enables experimenters to reliably estimate what new policies would accomplish, and to quantify their uncertainty when the network structure is only partially understood.