We consider estimating the total treatment effect (TTE) causal estimand in a setting with network interference. To model this interference, we introduce a potential outcomes framework where individuals receive additive effects for each sufficiently small subset of their neithborhood that is entirely exposed to a treatment. In this setting, we develop an unbiased estimator for TTE and reason about its variance. We validate our approach using experiments on simulated data.
Aug 4, 2023
We demonstrate the feasibility of using the high-resolution street-level photographs in Google Street View and an object-detection network (RetinaNet) to create a large-scale high-resolution survey of the prevalence of at least six plant species widely grown in road-facing homegardens in Thailand.
Jan 2, 2021