FUJITSU Project:
Title Enhancing explainability of causal discovery AI
Industrial Partner FUJITSU LIMITEDThe Fujitsu Group provides digital services globally, with operations in different regions around theworld, including Japan. Fujitsu’s Information Technology (IT) services business ranks at the top bymarket share in Japan and is in the top tier worldwide: a record that reflects our outstanding
technologies and long track record in building large-scale, cutting-edge systems.
Industrial Mentor
Hiroyuki Higuchi, Ph.D., Fujitsu Limited.
Background
Artificial Intelligence (AI) has been increasingly introduced into various industries. Nevertheless,certain challenges remain in applying AI and machine learning technologies to overcome
challenges in various fields. To identify key drivers of the difficulties to be overcome, to develop
strategies, and to take appropriate actions, it is necessary not merely to assess correlation
between attributes A and B, but also to ascertain the causal relation linking A to B, such as “A isthe cause of B.”
Recently several methods have been proposed for the discovery of causal structures fromobservational data [1,2]. Such methods generally estimate one common causal structure for an
entire set of data. However, causality might change or differ depending on the situation. For
example, in the case of cancer treatment in a medical setting, every patient has their own
expression of genes, which affects the disease state of cancer. Therefore, to devise appropriate
treatment plans for individual patients, doctors must identify genes which are specific to eachcancer patient, not genes that are common to all cancer patients.
Using Fujitsu’s Wide Learning technology [3,4], which finds all important combinations
without omission, Fujitsu has developed a causal discovery AI technology that cancomprehensively infer causal relations under specific conditions (Fig. 1).

Fig. 1 Fujitsu’s causal discovery.

(a) Common causal graph(b) Causal graph under the condition of"𝑐𝑜𝑙𝑜𝑟 = 𝑟𝑒𝑑 ∧ 𝑡𝑜𝑡𝑎𝑙 𝑠𝑢𝑙𝑓𝑢𝑟 𝑑𝑖𝑜𝑥𝑖𝑑𝑒 ≥ 130”
Fig. 2 Examples of causal graphs.

Fig. 2 depicts examples of causal graphs for the Wine Quality Data Set [5] from UCI datasets[6]. Whereas Fig. 2(a) shows a common causal graph generated using the DirectLiNGAM method[1], Fig. 2(b) shows a causal graph for a specific condition generated using Fujitsu’s causal
discovery AI technology. As shown in Fig. 2, a linear non-gaussian acyclic model (abbreviated asLiNGAM) [7] is assumed, in which the weight attached to an edge from node xi to node xj represents
the coefficient or the connection strength of xj on xi; that is, the weight represents change in thevalue of xj corresponding to unit change in the value of xi. In real-world problems, generally, whenthe number of nodes is large, each causal graph is complicated. Also, the number of causal graphs
under conditions is large. Therefore, the challenge is to ascertain how to extract important
information effectively and how to improve the explainability of causal discovery AI.

Project overview
In the project, you will develop an enhanced view of explanations of causal discovery AI assisted
by mathematics. For purposes of enhancement, you should consider several aspects such as
those presented below.
1.

Convincingness: Explanations must be convincing and accepted by users. Therefore,evaluating or estimating the explanations’ likelihood of convincing users is important. That
evaluation will require mathematical modeling of the explanation’s characteristic of“convincingness”. The simplest modeling would be to use a length of explanation based on
the assumption that a short explanation is easy to understand. However, better modelingmight be devised by assuming more practical recognition models.

2.

Variety: An explanation for causality is not always unique. Often, more than one perspective
is necessary to explain causality. The phenomenon by which many equally good models
explain some given data well is called the Rashomon effect [8]. A set of many equally goodmodels is called a Rashomon set [9]. Although a Rashomon set provides variety in explanation,a tradeoff between variety and simplicity (convincingness) should be considered.

3.

Discoverability: When an explanation is not convincing to a user, it is not always because it isa bad explanation. It might be a new finding that a user has never noticed. In that sense, anexplanation must also be evaluated in terms of discoverability.
By exploring one or more aspects of enhancement, you will develop a new way of giving

explanations of causal discovery AI.
Expectations
When participating in this project, to enhance the explainability of causal discovery AI, you will be
expected to devise a new user interface of causal discovery AI and then to implement it. We lookforward to welcoming students who are interested in statistical analysis and the Wide Learning
Website “Hello, Wide Learning!” [3]. In addition, students who are interested in answers to thefollowing questions are welcomed.
ü

What is explainable AI?

üWhat is the gap separating AI and humans?

ü

What are good interactions between AI and humans?

üHow can AI help humans discover new findings?

Requirements
Programming skills in python, and preferably C or C++.

References[1] S. Shimizu et al., “DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian
Structural Equation Model,” Journal of Machine Learning Research, vol. 12, pp. 1225-1248,
2011.
[2] A. Zanga et al., “A Survey on Causal Discovery: Theory and Practice,” International Journal ofApproximate Reasoning, vol. 151, pp. 101-129, 2022.
[3] Wide Learning Website, "Hello, Wide Learning!".
https://widelearning.labs.fujitsu.com/en/[4] G-RIPS Sendai 2023 Fujitsu Group Final Report.
https://www.mccs.tohoku.ac.jp/g-rips/report/2023/pdf/fujitsu_final_report.pdf[5] P. Cortez et al., Modeling wine preferences by data mining from physicochemical properties,Decision Support Systems, Elsevier, vol. 47, no. 4, pp. 547-553, 2009.
[6] D. Dua and C. Graff, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], 2019.
[7] S. Shimizu et al., “A Linear Non-Gaussian Acyclic Model for Causal Discovery,” Journal ofMachine Learning Research, vol. 7, pp. 2003-2030, 2006.
[8] L. Breiman, “Statistical modeling: The two cultures (with comments and a rejoinder by theauthor),” Statistical Science, vol. 16, no. 3, pp. 199-231, 2001.
[9] R. Xin et al., “Exploring the Whole Rashomon Set of Sparse Decision Trees,” In Proc. 36th Conf.
on Neural Information Processing Systems, 2022.