  Causal inference plays an important role in explanatory analysis and decision
making across various fields like statistics, marketing, health care, and
education. Its main task is to estimate treatment effects and make intervention
policies. Traditionally, most of the previous works typically focus on the
binary treatment setting that there is only one treatment for a unit to adopt
or not. However, in practice, the treatment can be much more complex,
encompassing multi-valued, continuous, or bundle options. In this paper, we
refer to these as complex treatments and systematically and comprehensively
review the causal inference methods for addressing them. First, we formally
revisit the problem definition, the basic assumptions, and their possible
variations under specific conditions. Second, we sequentially review the
related methods for multi-valued, continuous, and bundled treatment settings.
In each situation, we tentatively divide the methods into two categories: those
conforming to the unconfoundedness assumption and those violating it.
Subsequently, we discuss the available datasets and open-source codes. Finally,
we provide a brief summary of these works and suggest potential directions for
future research.
