Causal Effect Identification in Uncertain Causal Networks

TL;DR

This paper addresses causal effect identification in uncertain causal networks by formulating an NP-complete optimization problem and proposing efficient approximation algorithms, with evaluations on real and synthetic data.

Contribution

It introduces the edge ID problem for uncertain causal graphs and provides approximation algorithms to identify plausible subgraphs with identifiable causal effects.

Findings

The edge ID problem is NP-complete.

Proposed algorithms effectively approximate the solution.

Algorithms perform well on real-world and synthetic networks.

Abstract

Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correctly specified causal structure. In this work, we study the setting where a probabilistic model of the causal structure is available. Specifically, the edges in a causal graph exist with uncertainties which may, for example, represent degree of belief from domain experts. Alternatively, the uncertainty about an edge may reflect the confidence of a particular statistical test. The question that naturally arises in this setting is: Given such a probabilistic graph and a specific causal effect of interest, what is the subgraph which has the highest plausibility and for which the causal effect is identifiable? We show that answering this question reduces to solving an NP-complete combinatorial optimization problem which we call the edge ID problem. We propose efficient algorithms to approximate this problem and evaluate them against both real-world networks and randomly generated graphs.