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Investigate causes and find solutions from data easily
The AI tool that allows you to use the knowledge of difficult evidence acquisition without experts

USERS

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xCausal responds to these concerns.

Unable to discover the cause of
issues using data.

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Discovering cause candidates and estimating effects leads to quick solutions and actions.

R&D and product planning are not
progressing due to lack of data analysts.

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Can easily discover hypotheses even not a data analysis professional.

Want to systemize the personal
know-how and make it usable.

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Tacit knowledge within an organization can be capitalized as causal models.

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Causal AI SaaS platform

Causal AI (AI based on cause-and-effect relationships)
identifies causes and drives action

Even when data is visualized and predictive models are created to solve business issues, there are no discoveries that lead to action. This is a surprisingly common issue when adopting DX and AI. The key is to understand the "causal relationship". While AI excels at discerning patterns based on data correlations, it often falls short in elucidating why certain outcomes occur. On the other hand, in business, it is important to find out what to do to improve results, that is, to understand the causes and identify actions. xCausal is an innovative tool enabling users to swiftly grasp the cause-and-effect mechanisms by deducing causal links from data without specialized knowledge to obtain academic evidence. Additionally, it can simulate the impact of interventions, answering the question, "If I adjust this parameter, what will be the resulting effectiveness?" AI x Causal (AI enhanced by causality). This tool empowers the realization of "reliable AI," paving the way for a brighter future.

※ Preparations are required depending on the purpose of use, such as having domain knowledge experts and collecting necessary data.

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Need to look at causation, not correlation

"The sun will rise soon after the rooster crows" is an example of a prediction based on correlation. However, even if the rooster is gone, the sun will still rise. In other words, this relationship is not causal.Thus, current AI recognizes patterns by using both causal and non-causal variables. There are many applications where this method is fine. On the other hand, in areas such as business and healthcare, it is often important to find out what can be done to improve outcome and take action. This means that it is important to understand the cause-and-effect that are influencing the results. For example, even if the correlation is high and it appears to be a cause-and-effect relationship, in reality there are many cases where a common cause "confounding factor" that affects both variables is creating a "pseudo-correlation." A well-known illustration of this is the apparent correlation between chocolate consumption in a country and the number of Nobel Prize laureates. However, despite the association, increased chocolate consumption does not result in more Nobel Prizes. This is likely due to other factors affecting both variables, such as GDP per capita, for example.Understanding cause-and-effect relationships is the key to success in deriving solutions and actions from data.

Correlation is not causation

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Why xCausal™ ?

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"Smallytics™" algorithm that makes it easy to use "Causal Discovery" which requires supercomputer class processing

Discovering the structure of causal relationships from data is called "Causal Discovery," and estimating causal effects is called "Causal Inference." When the structure of the causal relationship is known, "Causal Inference" can be conducted promptly. However, in many cases, uncovering unknown relationships drives innovation. In such instances, "Causal Discovery" proves to be a valuable tool. Yet, until now, this approach has been impractical due to the substantial increase in processing time required to compute all data combinations when estimating causal structures. This process could take hours, days, or even necessitate the use of a supercomputer in extreme cases. xCausal™ addresses this challenge with its unique Smallytics™ algorithm. This algorithm assesses and prioritizes data interrelationships, considering the assumptions underlying causal relationships across various combinations of time-series data.

Smallytics™ suggests variables that are strongly related to the data you are analyzing. Users can then refine their selection by choosing the most meaningful variables (up to 10 nodes, customization available). As a result, it is possible to dramatically reduce the amount of combinatorial calculations and instantly generate causal graphs with network structures in tens of seconds or minutes. Therefore, users can iterate through trial and error with data to understand cause-and-effect relationships and formulate high-resolution hypotheses for solutions. Users can select from three types of causal discovery algorithms: Score based, constraint based, and hybrid.

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A future-oriented SaaS service that makes academic know-how easy to use and has excellent functionality enhancement and scalability.

"xCausal™" is a SaaS that can be used by uploading data. xCausal™ allows users to easily perform missing data completion and create rule tables when uploading data. Smallytics™, the analysis engine, is resilient to missing data and offers user-friendly functionality, allowing users to utilize it without investing time in the data cleansing process typically required for statistical analysis and AI. Another unique feature is its intuitive and sophisticated UI design, making it user-friendly despite the complexity often associated with such interfaces. After data upload, "Causal Discovery" and "Causal Inference" can be quickly and seamlessly performed, facilitating the application of academic expertise.

Compared to PC-installed software, this forward-thinking solution offers scalability in processing, facilitates collaborative data analysis, and provides continuous updates to evolving Causal AI functions. It is also more practical than software installed on in-house cloud systems, as it does not require coding implementation and can be used without incurring engineering costs.

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Customization, Accompaniment Professional Services and Partnerships.

Depending on how "Causal Discovery" and "Causal Inference" are utilized, arriving at a viable solution may be challenging. Specifically, without obtaining the data potentially causing the issue, identifying a causal relationship becomes improbable. We provide professional services including consulting, data analysis, and customization to resolve these customer issues. Our advisory committee, comprising professors and associate professors from leading universities and research institutions, conducts cutting-edge research in various data science areas, including causal inference, machine learning, statistical analysis, and LLM, continuously generating new knowledge. Apart from customizing xCausal™ functionality, the system also facilitates diverse development avenues, such as applying outputs from causal models, integrating them with predictive models, and utilizing them alongside generative AI. Moreover, we aim to enhance our contribution to resolving client issues by collaborating with partner companies possessing expertise across diverse industries and domains in the near future.

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Document Download

Usage

xCausal can be customized and used in a variety of industries, including the wellness and healthcare industry, where there is a need to investigate causes and identify solutions.

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  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)
  1. Quality improvement, failure prediction, and accident rate reduction in the manufacturing industry
  2. Operational volume prediction and supply chain optimization in the transportation and retail industries
  3. Fraud detection, new product development at financial institutions and insurance companies
  4. Smart city planning and operation, EBPM by governments and local governments

For Wellness & Healthcare

This service is for the wellness and healthcare sector. It supports the collected lifelog data from users to provide collaborative apps, ready-to-use dashboards and causal inference services.

For Business & Marketing

Customization is available for areas beyond wellness. xCausal is effective in supporting business decisions and problem-solving. Examples include enhancing manufacturing quality, reducing accident rates, optimizing supply chains, improving customer satisfaction, managing churn, optimizing marketing mix models, and detecting fraud.

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Service Overview and Usage

xCausal is a SaaS-style service for causal inference.
After uploading and setting up your data, you simply use the service in the following steps.

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1

Select Analysis Item

Select the data items to be analyzed and use our relationship strength detection algorithm, "Smallytics™", to select the analysis targets.

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2

Generate a causal graph

Generate a causal graph, observe its structure, and select nodes of interest that may have an influence.

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Virtual verification of causal effects

Mouse over the node and click on it. Mouse over the node and virtually change the value (intervention) to predict the distribution after the intervention for analysis purposes.

Screen Images of xCausal

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Data integration

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Dashboard

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Smallytics selection screen

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Graph generation screen

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Before intervention

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After intervention

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Innovation accelerating with causal relationship simulation

Companies and research institutes routinely create hypotheses for solutions to problems and research themes, and search for solutions while testing them. In particular, in R&D departments and planning departments, human resources with advanced knowledge repeat long verification cycles after discovering a correlation until a certain level of evidence is obtained. This process takes months to years.

Obtaining evidence is essentially about demonstrating the causal effectivity of a solution. xCausal integrates cutting-edge causal structure search technology with our proprietary expertise to generate a digital replica of causal relationships. This enables us to conduct virtual trial-and-error experiments rapidly, continuously refining our approach minute by minute. Such iterative testing facilitates early failure experiences and leads to more refined hypotheses through methods like stratification. This accelerates the R&D cycle, boosts success rates, and optimizes the utilization of highly skilled human resources.

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The VELDT Method for Realizing "Responsible AI"

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Currently, AI is a correlation-based technology and although it can make predictions, it is unclear why those predictions were made. For this reason, technologies have emerged to provide explanations, such as XAI. (ExplainableAI) and other technologies are emerging to provide explanations. Especially today, when "Responsible AI" is required, it is important to be able to interpret and explain. We have formulated the VELDT Method, a methodology to concretely realize "Responsible AI" and are promoting development in the area of Causal AI (Causal AI), which utilizes causal reasoning in AI to provide interpretability and explainability.

xCausal
For Wellness & Healthcare