Counterfactual Reasoning in Causal Inference: Investigating counterfactual reasoning methods for causal inference tasks in machine learning applications

Abstract

Counterfactual reasoning plays a crucial role in causal inference, enabling us to understand the effects of actions and interventions in complex systems. In the context of machine learning, counterfactual reasoning is essential for making decisions based on causal relationships inferred from data. This paper provides an overview of counterfactual reasoning methods and their applications in causal inference tasks in machine learning. We discuss the challenges and limitations of current approaches and propose future research directions to improve the robustness and effectiveness of counterfactual reasoning in causal inference.