Linear-Time Primitives for Algorithm Development in Graphical Causal Inference

TL;DR

CIfly is a new framework that simplifies and accelerates graphical causal inference algorithms by focusing on reachability computations, offering linear-time performance and practical implementations.

Contribution

The paper introduces CIfly, a novel framework that formalizes reachability-based primitives for causal inference, providing linear-time algorithms and an open-source implementation.

Findings

CIfly outperforms traditional primitives like moralization and latent projection.

The framework enables re-implementation of existing causal inference tasks efficiently.

CIfly supports new algorithms for instrumental variables.

Abstract

We introduce CIfly, a framework for efficient algorithmic primitives in graphical causal inference that isolates reachability as a reusable core operation. It builds on the insight that many causal reasoning tasks can be reduced to reachability in purpose-built state-space graphs that can be constructed on the fly during traversal. We formalize a rule table schema for specifying such algorithms and prove they run in linear time. We establish CIfly as a more efficient alternative to the common primitives moralization and latent projection, which we show are computationally equivalent to Boolean matrix multiplication. Our open-source Rust implementation parses rule table text files and runs the specified CIfly algorithms providing high-performance execution accessible from Python and R. We demonstrate CIfly's utility by re-implementing a range of established causal inference tasks within the framework and by developing new algorithms for instrumental variables. These contributions position CIfly as a flexible and scalable backbone for graphical causal inference, guiding algorithm development and enabling easy and efficient deployment.