Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models

TL;DR

This paper introduces a new class of acyclic mixed graphs for causal modeling, providing graphical criteria for effect identification, algorithms for learning from data, and gated models that leverage context-specific independences.

Contribution

It proposes a novel family of acyclic mixed graphs, offers criteria for causal effect identification, and develops algorithms for learning and extending causal inference with gated models.

Findings

Provided graphical criteria for causal effect identification.

Developed an exact learning algorithm using answer set programming.

Introduced gated models exploiting context-specific independences.

Abstract

We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identification of arbitrary causal effects when the new models contain directed and undirected edges but no bidirected edge. We also provide a necessary and sufficient graphical criterion for the identification of the causal effect of a single variable on the rest of the variables. Moreover, we develop an exact algorithm for learning the new models from observational and interventional data via answer set programming. Finally, we introduce gated models for causal effect identification, a new family of graphical models that exploits context specific independences to identify additional causal effects.