New brand "xCausal" will be launched on October 6th

On October 6th, 2023, VELDT Inc.(Head Office: Shibuya-ku, Tokyo; CEO: Jin Nonogami), CausalAI (Causal Inference AI) solution provider has further evolved its agile causal inference platform "DataEthnography" that accelerate the discovery of solutions and ideation by infering causal relationships, renew the service as "xCausal". The service can be used in a wide range of areas where causes and solutions are needed, including manufacturing, finance, logistics, marketing, and so on. (Service website: https://xcausal.com/en/)

In recent years, Japan and OECD member countries are facing a declining labor force due to the aging of the population and falling birthrate, and the use of digital technology, especially AI technology, is expected to be utilized in order to efficiently produce output even with a small labor force. While AI technologies such as generative AI are attracting attention, there is a concern about black box and unfamiliar technologies in the background, as indicated by a Reuters Ipsos poll*1 that more than 60% of Americans feel threatened by AI.

*1 See: https://jp.reuters.com/article/tech-ai-poll-idCAKBN2X80ST

The output of general AI are based on pattern recognition through correlation, and although it can predict and classify, it cannot know "what can be done to improve the results" because it does not know the causes for the results. In order to solve this black box problem, we have developed and are continuously enhancing our reliable AI "xCausal" by adding an understanding of cause-and-effect relationships such as why the results were calculated and where users can improve ?.

■In addition to "visualization", "action" is important for DX : Expanding into areas where causes and solutions are needed

To date, we have developed our services with a focus on the wellness and healthcare domains.

On the other hand, many other companies and government agencies that are promoting DX have voiced that although they can "visualize" the current status using data, they do not know what they need to do to change the results, and this "does not lead to action". We have been receiving requests for this solution to be used in areas other than wellness and healthcare.

xCausal can be customized for use in a wide range of industries that require understanding of causes and identification of solutions as follows.

1. Wellness and healthcare
2. Acceleration of R&D
3. Drug discovery in the pharmaceutical industry
4. Reduction of GHG emissions
5. Customer satisfaction improvement and churn management
6. Optimization of promotion budget (MMM)
7. Quality improvement, failure prediction, and accident rate reduction in the manufacturing industry
8. Operational volume prediction and supply chain optimization in the transportation and retail industries
9. Fraud detection, new product development at financial institutions and insurance companies
10. Smart city planning and operation, EBPM by governments and local governments

In addition to providing integrated solution with existing AI and XAI (explainable AI), various new functions, such as counterfactual processing, will be sequentially expanded in the future.

■What you can do with "xCausal"

xCausal is an easy-to-use SaaS-type causal inference platform that can be used by uploading data. With Professional Services, xCausal can be customized to meet your specific needs. xCausal recommends variables that are strongly related to the results you want to improve based on causal assumptions, and visualizes the structure of causal relationships in as little as a few dozen seconds (Causal Discovery). Furthermore, it is also possible to virtually change variables that are thought to be the cause of a problem and simulate how the results change (Causal Inference). This allows you to confirm the causal relationship and then see the effect, leading to an understanding of the cause and a decision on the action to be taken. The latest version adds the following features (Advanced option only)

Causal Discovery algorithm selection

1. You can choose from multiple causal discovery algorithms to calculate the structure of causal relationships.

Stratification function

1. Stratification processing function that allows you to set groups, calculate causal effects, and compare them.

■Expert Comments

Ryoichi Shinkuma, Professor, Department of Information Engineering, Shibaura Institute of Technology

The relationship between people and events must be unraveled in order to understand their essence. To elucidate relationships, especially causal relationships, xCausal incorporates cutting-edge science from world-class experts. Intervention features allow us to simulate what would happen if this were the case? The intervention feature enables simulations of what would happen if this were the case, and is expected to be widely used in the coming digital twin era.

Masaaki Imaizumi, Associate Professor, Komaba Institute for Science, Graduate School of Arts and Science, The University of Tokyo

I feel that xCausal achieves both correct statistical causal inference and a superior user experience at a high level. In other words, xCausal provides users with useful information in an easy-to-use interface while correctly utilizing data analysis techniques for statistical causal inference. The company is also actively introducing cutting-edge technology, and I look forward to its continued development in the future.