  Estimates of individual treatment effects from networked observational data
are attracting increasing attention these days. One major challenge in network
scenarios is the violation of the stable unit treatment value assumption
(SUTVA), which assumes that the treatment assignment of a unit does not
influence others' outcomes. In network data, due to interference, the outcome
of a unit is influenced not only by its treatment (i.e., direct effects) but
also by others' treatments (i.e., spillover effects). Furthermore, the
influences from other units are always heterogeneous (e.g., friends with
similar interests affect a person differently than friends with different
interests). In this paper, we focus on the problem of estimating individual
treatment effects (both direct and spillover effects) under heterogeneous
interference. To address this issue, we propose a novel Dual Weighting
Regression (DWR) algorithm by simultaneously learning attention weights that
capture the heterogeneous interference and sample weights to eliminate the
complex confounding bias in networks. We formulate the entire learning process
as a bi-level optimization problem. In theory, we present generalization error
bounds for individual treatment effect estimation. Extensive experiments on
four benchmark datasets demonstrate that the proposed DWR algorithm outperforms
state-of-the-art methods for estimating individual treatment effects under
heterogeneous interference.
