LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments

TL;DR

This paper introduces LazyIter, an efficient algorithm for counting Markov Equivalence Classes (MECs) and designing experiments, significantly improving computational speed and enabling better causal discovery from observational and intervention data.

Contribution

The paper presents LazyIter, a novel algorithm that reduces MEC size computation complexity and facilitates optimal experiment design, outperforming existing methods.

Findings

LazyIter reduces MEC size computation time by a factor of O(n) for sparse graphs.

The algorithm enables efficient active and passive experiment design.

Experimental results demonstrate superior performance over state-of-the-art methods.

Abstract

The causal relationships among a set of random variables are commonly represented by a Directed Acyclic Graph (DAG), where there is a directed edge from variable $X$ to variable $Y$ if $X$ is a direct cause of $Y$. From the purely observational data, the true causal graph can be identified up to a Markov Equivalence Class (MEC), which is a set of DAGs with the same conditional independencies between the variables. The size of an MEC is a measure of complexity for recovering the true causal graph by performing interventions. We propose a method for efficient iteration over possible MECs given intervention results. We utilize the proposed method for computing MEC sizes and experiment design in active and passive learning settings. Compared to previous work for computing the size of MEC, our proposed algorithm reduces the time complexity by a factor of $O(n)$ for sparse graphs where $n$ is the number of variables in the system. Additionally, integrating our approach with dynamic programming, we design an optimal algorithm for passive experiment design. Experimental results show that our proposed algorithms for both computing the size of MEC and experiment design outperform the state of the art.