What is Causality in Artificial Intelligence?
Artificial Intelligence & Machine Learning
Causality/Causal inference in Artificial Intelligence
“While probabilities encode our beliefs about a static world, causality tells us whether and how probabilities change when the world changes, be it by intervention or by act of imagination.” – UCLA Professor & A.M. Turing Award Winner Judea Pearl
“Causality is tied to the basic goal of science – encapsulated in the Greek phrase “Ceteris Paribus”, which means “holding all else equal”. A scientific law produces an understanding that lets us change one variable in a complex system and examine how the system changes while holding all other variables equal. The discovery, validation, and measurement of such laws is one of the basic goals of scientific exploration”. -Wharton Professor Amit Gandhi
“Disentangling causality from correlation is the greatest challenge for any who would make their decisions based on data.” – University of Florida Professor Paul Borochin
Causality is the process of inferring causes from data, according to Wang (2020). Humans apply causal inference every day to solve problems. Causality is an essential element of artificial intelligence (AI) because cause-effect relationships contribute significantly to rational decision-making. For Schuller and Haberi (2016), causality is a concept of human thinking that helps answer why phenomena occur and how we interact with our environment. Dickson (2021) deduces that humans intuitively infer causality and develop the ability to do so at a very young age through observing the world. In addition, Dickson (2021) and Wang (2020) agree that it remains to implement causality in machine learning algorithms, and they want to evaluate the success of causality in AI.
Causality in AI is currently in its infancy, although some companies have notably innovated to enact causality in AI. Notably, causaLens is leading in causal AI and has enacted the autonomous finding of critical information in limited data and harnessing of human knowledge, furthermore, which allows AI machines to make recommendations with small datasets, that are ubiquitous in business and government.
The predictive analytics side of the AI industry remains in its growth phase. However, the health insurance sector exemplified issues with predictive analytics. Moreover medication-predicting algorithms perpetuated racial biases towards black patients (Sgaier, Huang & Charles, 2020). In this case, causation was inferred from correlation, which highlighted the importance of AI better understanding causality – the precise relationship between cause and effect – to make more accurate decisions (Sgaier, Huang & Charles, 2020).
Causality vs Correlation in Machine Learning with causaLens CEO Darko Matovski
To correctly predict causality, causal AI must identify the root causes of outcomes, model interventions that could change outcomes, and answer what-if questions. When modeling outcomes in uncertain situations, machine learning must stay consistent with subtle changes in a problem’s distributions (IEEE, n.d.). By conveying information in step-by-step fault tree analysis, causal AI improves transparency, which allows for easier human understanding and trust in the rationale for decisions (Greifenede, 2021). The ability to reproduce the results with ensured high accuracy is another benefit of causal AI in machine learning. Thirdly, causal AI reduces bias by evaluating the full context of the occurrence and by providing explanations of the rationale behind decisions.
These causal AI benefits have so far been employed in different sectors, including asset management, capital markets, manufacturing, retail banking, healthcare, insurance, and telecommunications. causaLens has provided industry and service-related causal AI products in each sector.
Real-Time Data
To achieve these benefits, causal AI must utilize real-time updates to understand the process better and determine precise root causes and the reasons for its decisions. Causal AI must also learn the invariant relationships in data that are invariant in different contexts to be robust to changing conditions. Causal AI must receive human contexts, goals, and metrics to make better cause-effect predictions.
Overall, causal AI makes machine learning robust, explainable, and valuable. While the technology is in its development, causaLens – an industry leader – has demonstrated the various benefits of causal AI and its diverse applications. Lastly, to enable causality, AI must receive a plethora of pertinent information to determine root causes and utilize these root causes to inform its decisions.
In conclusion, we spoke with one of the hottest Machine Learning firms around causaLens and they told us:
“Causal AI is seeing a global revolution. AI leaders in both academia and industry are recognising causality as the key to make AI that is truly intelligent and doesn’t fail when applied to the real world. Our customers are already seeing the transformational benefits of this technology and our goal is to expand its reach to tackle the greatest challenges in the economy, society, and healthcare” – explains causaLens CEO Darko Matovski.
Furthermore, causaLens’ Ben Steiner added:
“Our partners come to us for a range of reasons: they want more explainability, decision enhancement, better human-machine integration, they want to tackle AI bias, or work with small data. Disillusionment with correlation-based machine learning is the common denominator. Causality is the missing ingredient that helps to solve all these problems with current enterprise AI.”
See Ben live next week at the Cornell Financial Engineering Manhattan 2022 Future of Finance Conference!
References:
CuasaLens. (n.d.). Causal AI: The next generation of Enterprise AI. https://www.causalens.com/why-causal-ai/
Dickson, B. (March 15, 2021). Why machine learning struggles with causality. TechTalks. https://bdtechtalks.com/2021/03/15/machine-learning-causality/
Greifeneder, B. (November 1, 2021). Three ways a causal approach can improve trust in AI. Forbes. https://www.forbes.com/sites/forbestechcouncil/2021/11/01/three-ways-a- causal-approach-can-improve-trust-in-ai/
IEEE. (n.d.). What is ‘causal inference; and why is it key to machine learning? IEEE Innovation at Work. https://innovationatwork.ieee.org/what-is-causal-inference-and-why-is-it-key- to-machine-learning/
Schuller, M. & Haberi, A. (2016). Causality Techniques in investment Management: Five Key Findings. CFA Institute. https://blogs.cfainstitute.org/investor/2022/03/16/causality- techniques-in-investment-management-five-key-findings/
Sgaier, S. K., Huang, V., & Charles, G. (2020). The Case for Causal AI. Stanford Social Innovation Review, 18(3), 50–55. https://doi.org/10.48558/KT81-SN73
Wang, K. (March 6, 2020). Causal Inference: Trying to understand the question of why. Todays Data Science. https://towardsdatascience.com/implementing-causal-inference-a-key-step- towards-agi-de2cde8ea599
