Charles Assaad, IPLESP, Causal inference using DAGs and SCGs

Abstract:
Structural Causal Models (SCMs) offer a powerful framework for understanding and reasoning about causal relationships, particularly in the context of total effects. In this presentation, we will explore a range of established tools for identifying total effects using fully specified Directed Acyclic Graphs (DAGs) derived from SCMs. These tools will be illustrated through two epidemiological studies that demonstrate their practical application.

However, many fields—such as epidemiology and genetics—encounter significant challenges in fully specifying these graphs. To address these limitations, we introduce Summary Causal Graphs (SCGs), an abstraction of DAGs that are simpler to construct. Unlike traditional DAGs, SCGs may include cycles and vertices that do not correspond directly to individual random variables, making them more flexible for complex systems.

Additionally, we will present novel methods for reasoning about and identifying total effects using SCGs, even in scenarios involving cycles, thereby extending the applicability of causal inference to less structured settings.