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CausaLens gets $45M for no-code technology that introduces cause and effect into AI decision making – TechCrunch

Written by corres2

One of the most popular applications of artificial intelligence to date has been to use it to predict things, using algorithms trained with historical data to determine a future outcome. But popularity doesn’t always mean success: predictive AI leaves out a lot of the nuance, context and cause-and-effect reasoning that goes into an outcome; and as some have pointed out (and as we have seen), this means that sometimes the “logical” answers produced by predictive AI can prove disastrous. A startup called causaLens has developed causal inference technology — presented as a no-code tool that doesn’t require a data scientist to use to introduce more nuance, reasoning and cause-and-effect sensibility into an AI-based system — which it believes can solve this problem.

CausaLens’s aim, CEO and co-founder Darko Matovski said, is for AI “to start to understand the world as humans understand it.”

Today the startup is announcing $45 million in funding after seeing some early success with its approach, growing revenues 500% since coming out of stealth a year ago. This is being described as a “first close” of the round, meaning it’s still open and potentially going to grow in size.

Dorilton Ventures and Molten Ventures (the VC that rebranded from Draper Esprit) led the round, with previous backers Generation Ventures and IQ Capital, and new backer GP Bullhound also participating. Sources tell us the round values London-based causaLens at around $250 million.

CausaLens’s customers currently include organizations in healthcare, financial services and government, among a number of other verticals, where its technology is used not just for AI-based decision making but to bring in more cause-and-effect nuance when arriving at outcomes. Critically, the

An illustrative example of how this works can be found in the Mayo Clinic, one of the startup’s customers, which has been using causaLens to identify biomarkers for cancer.

“Human bodies are complex systems, and so applying basic AI paradigms you can find any pattern you want, correlations of any sort, and you are not getting anywhere,” Darko Matovski, the CEO and founder of the startup, said in an interview. “But if you apply cause and effect techniques to understand the mechanics of how different bodies work, you can understand more of the true nature, of how one part has an impact on another.”

Considering all of the variables that might be involved, it’s the kind of big data problem that’s nearly impossible for a human, or even a team of humans, to compute, but is table stakes for a computer to work through. While it is not a cure for cancer, this kind of work is a significant step towards starting to consider different treatments tailored to the many permutations involved.

CausaLens’s tech has also been applied in a less clinical way in healthcare. A public health agency from one of the world’s biggest economies (causaLens cannot disclose publicly which one) used its causal AI engine to determine why certain adults have been holding back from getting Covid-19 vaccinations, so that the agency could devise better strategies to get them on board (plural “strategies” is the operative detail here: the whole point is that it’s a complex issue involving a number of reasons depending on the individuals in question).

Other customers in areas like financial services have been using causaLens to inform automated decision-making algorithms in areas like loan evaluations, where previous AI systems were introducing bias into its decisions when using historical data alone. Hedge funds, meanwhile, use causaLens to gain better understandings how a market trend might develop to inform their investment strategies.

And interestingly, one new wave of customers might be cropping up in the world of autonomous transportation. This is one area where the lack of human reasoning has held back progress in the field.

“No matter how much data is fed into autonomous systems, it’s still just historical correlations,” Matovski said of the challenge. He said that causaLens is in conversations now with two major automotive companies, with “many use cases” for its tech, but on in particular on autonomous driving “to help the systems understand how the world works. It’s not just correlated pixels related to a red light and a car stopping, but also what the effect will be of that car slowing down at a red light. We are bringing reasoning into the AI. Causal AI is the only hope for autonomous driving.”

It seems like a no-brainer that those using AI in their work would want the system to be as accurate as possible, which begs the question of why the brilliant improvement of causal AI hasn’t been built into AI algorithms and machine learning in the first place.

It’s not that more reasoning and answering “why” weren’t priorities early on, Matovski explained — “People have been exploring cause and effect relationships in science for a long time. You could even argue Newton’s equations are causal. It is super fundamental in science,” he said — but it’s that AI specialists couldn’t understand how to teach machines to do this. “It was just too difficult,” he said. “The algorithms and technology didn’t exist.”

That started to change around 2017, he said, as academics started to publish initial approaches considering how to represent “reasoning” and cause and effect in AI based on finding signals that contributed to existing outcomes (rather than using historical data to determine outcomes), and building models based on that. Interestingly, it’s an approach that Matovski says does not need to ingest huge volumes of training data to work. CausaLens’ team is very heavy on PhDs (you could say that the startup really ate its dogfood here: it considered 50,000 resumes while assembling its team). And this team has taken that baton and run with it. “Since then, it’s been an exponential growth curve” in terms of discovery, he said. (You can read more about it here.)

As you might expect, causaLens is not the only player out there looking at how to leverage advances in causal inference in bigger projects that rely on AI. Microsoft, Facebook, Amazon, Google and other big tech players with substantial AI investments are also working on the field. Among startups, there is also Causalis focusing specifically on the opportunity of using causal AI in medicine and healthcare, and Oogway appears to be building a causal AI platform geared at consumers, a “personalised AI decision assistant” as it describes itself. All of this speaks to the opportunity to develop more and a pretty massive market for the technology, covering both specific commercial and more general use cases.

“AI must take the next step towards causal reasoning to meet its potential in the real world. causaLens is the first to leverage Causal AI to model interventions and enable machine-driven introspection,” said Daniel Freeman of Dorilton Ventures, in a statement. “This world-class team has built software with the sophistication to win over serious data scientists and the usability to empower business leaders. Dorilton Ventures is very excited to support causaLens on the next stage of its journey.”

“Every company will adopt AI, not just because they can, but because they must,” added Christoph Hornung, an investment director at Molten Ventures. “We at Molten are convinced that causality is the key ingredient that’s needed to unlock the potential of AI. causaLens is the world’s first causal AI platform with a proven ability to convert data into optimal business decisions.”


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