  Data-driven societal event forecasting methods exploit relevant historical
information to predict future events. These methods rely on historical labeled
data and cannot accurately predict events when data are limited or of poor
quality. Studying causal effects between events goes beyond correlation
analysis and can contribute to a more robust prediction of events. However,
incorporating causality analysis in data-driven event forecasting is
challenging due to several factors: (i) Events occur in a complex and dynamic
social environment. Many unobserved variables, i.e., hidden confounders, affect
both potential causes and outcomes. (ii) Given spatiotemporal non-independent
and identically distributed (non-IID) data, modeling hidden confounders for
accurate causal effect estimation is not trivial. In this work, we introduce a
deep learning framework that integrates causal effect estimation into event
forecasting. We first study the problem of Individual Treatment Effect (ITE)
estimation from observational event data with spatiotemporal attributes and
present a novel causal inference model to estimate ITEs. We then incorporate
the learned event-related causal information into event prediction as prior
knowledge. Two robust learning modules, including a feature reweighting module
and an approximate constraint loss, are introduced to enable prior knowledge
injection. We evaluate the proposed causal inference model on real-world event
datasets and validate the effectiveness of proposed robust learning modules in
event prediction by feeding learned causal information into different deep
learning methods. Experimental results demonstrate the strengths of the
proposed causal inference model for ITE estimation in societal events and
showcase the beneficial properties of robust learning modules in societal event
forecasting.
