  A further understanding of cause and effect within observational data is
critical across many domains, such as economics, health care, public policy,
web mining, online advertising, and marketing campaigns. Although significant
advances have been made to overcome the challenges in causal effect estimation
with observational data, such as missing counterfactual outcomes and selection
bias between treatment and control groups, the existing methods mainly focus on
source-specific and stationary observational data. Such learning strategies
assume that all observational data are already available during the training
phase and from only one source. This practical concern of accessibility is
ubiquitous in various academic and industrial applications. That's what it
boiled down to: in the era of big data, we face new challenges in causal
inference with observational data, i.e., the extensibility for incrementally
available observational data, the adaptability for extra domain adaptation
problem except for the imbalance between treatment and control groups, and the
accessibility for an enormous amount of data. In this position paper, we
formally define the problem of continual treatment effect estimation, describe
its research challenges, and then present possible solutions to this problem.
Moreover, we will discuss future research directions on this topic.
