Causal Inference with Complex Treatments: A Survey

Causal inference plays an important role in explanatory analysis and decision-making across a wide range of fields, including statistics, marketing, healthcare, and education. Its core objective is to estimate treatment effects and inform intervention policies. Most existing work focuses on the binary treatment setting, where each unit is assigned to either treatment or control. In practice, however, treatments are often more complex, encompassing multi-valued, continuous, or bundle interventions. We refer to such settings as complex treatments. In this article, we provide a systematic and comprehensive survey of causal inference methods for complex treatments. We first revisit the problem formulation, core assumptions, and their possible variations under different settings. We sequentially review the representative methods for multi-valued, continuous, and bundle treatments. Within each setting, we organize the methods into two broad categories: those that rely on the unconfoundedness assumption and those that address violations of this assumption. We further discuss the intrinsic relationships among these methods and the assumption verification. Finally, we summarize available benchmark datasets and open-source codes, and outline several directions for future research.