  The Linear Threshold Model is a widely used model that describes how
information diffuses through a social network. According to this model, an
individual adopts an idea or product after the proportion of their neighbors
who have adopted it reaches a certain threshold. Typical applications of the
Linear Threshold Model assume that thresholds are either the same for all
network nodes or randomly distributed, even though some people may be more
susceptible to peer pressure than others. To address individual-level
differences, we propose causal inference methods for estimating individual
thresholds that can more accurately predict whether and when individuals will
be affected by their peers. We introduce the concept of heterogeneous peer
effects and develop a Structural Causal Model which corresponds to the Linear
Threshold Model and supports heterogeneous peer effect identification and
estimation. We develop two algorithms for individual threshold estimation, one
based on causal trees and one based on causal meta-learners. Our experimental
results on synthetic and real-world datasets show that our proposed models can
better predict individual-level thresholds in the Linear Threshold Model and
thus more precisely predict which nodes will get activated over time.
