TY - JOUR

T1 - Causal Effect Identification in Uncertain Causal Networks

AU - Akbari, Sina

AU - Jamshidi, Fateme

AU - Mokhtarian, Ehsan

AU - Vowels, Matthew J.

AU - Etesami, Jalal

AU - Kiyavash, Negar

N1 - Publisher Copyright:
© 2023 Neural information processing systems foundation. All rights reserved.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85184290919&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85184290919

SN - 1049-5258

VL - 36

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023

Y2 - 10 December 2023 through 16 December 2023

ER -