Causal Inference with Complex Treatments: A Survey

TL;DR

This survey comprehensively reviews causal inference methods for complex treatments, including multi-valued, continuous, and bundled options, highlighting their assumptions, datasets, and future research directions.

Contribution

It systematically categorizes and analyzes existing causal inference techniques for complex treatments, extending beyond traditional binary treatment frameworks.

Findings

Reviewed methods for multi-valued, continuous, and bundled treatments

Discussed assumptions and variations in causal inference methods

Identified datasets and open-source tools for complex treatment analysis

Abstract

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.