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Causal Inference 101

Causalis.ai is developing causal AI technology | lung cancer | StarFinder LabBy nature of its charter, StarFinder Lab gets to work with the best and brightest minds – people who seek to use their incredible knowledge and talent to transform healthcare.

To truly attain an in-depth understanding of the science behind the technologies being developed would take years of training at the highest level, as well as the aptitude to actually assimilate that specialized education. However, tכןhat doesn’t mean we can’t attempt to understand the fundamentals behind the technology, the logic, and the rationale behind its development or its intended impact.

Causalis.ai is a startup looking to make the application of causal inference to mass databases a reality. Causal inference is a statistical tool that enables artificial intelligence and machine learning algorithms to reason in similar ways. As the name implies, it deals with cause and effect.

Causal inference goes beyond correlation in that whereas correlation identifies the probability between events, causal inference looks at the actual cause and effect relationships. With the realm of personalized medicine upon us, Causalis.ai argues that by applying causal inference in predicting the efficacy and toxicity of oncological treatments on a personalized basis, we can empower both the oncologist and the patient to improve quality and length of life by making informed, collaborative decisions.

Causal inference has been around for decades, however, it is only now that we have the computing power and mathematics to utilize it to maximum effect. Finding causation is difficult, especially in an incredibly complex system like the human body with roughly 200 different types of cells, 25,000 genes, 30 trillion cells, and close to 40 trillion microbiome cells all interacting with one another. Add to this lifestyle and environment and we can begin to get an inkling into the scope of this task.

The artificial intelligence (AI) and machine learning revolution unfolding before our eyes are changing our lives in myriad ways, from performing complex surgeries to the automated driving of cars. However, AI has two weaknesses: a limited ability to track what the AI model is learning when applied to complex data; and limited ability in finding causation. Causal AI has the potential to overcome these weaknesses.

In healthcare, especially when providing treatment recommendations, it is essential to find cause and effect relationships in the data. We must be able to answer so that therapies would be effective and safe. If you know what positively affects a health condition, you have discovered therapy. If you know the health conditions the therapy you discovered may cause and under what circumstances, you determine its side effects and quality of life profile.

The importance of cause and effect for optimal healthcare should by now be apparent: thus pointing to Causal AI as the potential missing link to unlocking the bright future of personalized medicine.

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