Causal Inference Using Tractable Circuits

TL;DR

This paper explores how probabilistic inference in causal models can be made computationally tractable using arithmetic circuits, enabling efficient reasoning even in complex, previously intractable models.

Contribution

It introduces a novel approach to compile causal graphs into arithmetic circuits, supporting linear-time inference and parameter estimation without requiring known causal mechanisms.

Findings

Inference can be performed in linear time relative to circuit size.

Circuit size can be bounded independently of graph treewidth.

Method enables scalable causal inference in complex models.

Abstract

The aim of this paper is to discuss a recent result which shows that probabilistic inference in the presence of (unknown) causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This result was reported recently to facilitate model-based supervised learning but it can be interpreted in a causality context as follows. One can compile a non-parametric causal graph into an arithmetic circuit that supports inference in time linear in the circuit size. The circuit is also non-parametric so it can be used to estimate parameters from data and to further reason (in linear time) about the causal graph parametrized by these estimates. Moreover, the circuit size can sometimes be bounded even when the treewidth of the causal graph is not, leading to tractable inference on models that have been deemed intractable previously. This has been enabled by a new technique that can exploit causal mechanisms computationally but without needing to know their identities (the classical setup in causal inference). Our goal is to provide a causality-oriented exposure to these new results and to speculate on how they may potentially contribute to more scalable and versatile causal inference.