Causal AI goes beyond standard predictive analytics, directly augmenting human decision-making. Its recommendations are intrinsically explainable, reliable in real-world scenarios, and sensitive to business and governance constraints.
Causal AI plays an ever more important role in our investment analysis. It empowers our strategists and portfolio managers to generate alpha by identifying new causal relationships in economic, financial and alternative data, with sophisticated, adaptive and explainable models that don’t suffer from overfitting.”
The causaLens platform has enabled us to discover additional value in our data. Their causal AI technology autonomously finds valuable signals in huge datasets and has helped us to understand relationships between our data and other datasets.”
causaLens provides us with key causal insights that continuously unlock untapped value in our data.”
Transparency and explainability of AI models requires an understanding of causality—an inherent advantage of the causaLens platform”
The novel Causal AI techniques available on the causaLens Platform have facilitated our joint effort of discovering valuable profitable trading strategies”
causaLens’ causality-based techniques & automation of quantitative workflows help us discover more orthogonal signals faster while discarding spurious correlations”
Causal AI is a fundamental scientific breakthrough and causaLens’ vision for Causal AI extends far beyond enterprise decision making. causaLens has the potential to disrupt a vast range of sectors and industries and has already demonstrated the value of its Causal AI technology in biological applications such as the discovery of cancer biomarkers”
*This should be taken as a personal statement and not a statement or endorsement from Mayo Clinic. Mayo Clinic does not support or endorse any commercial products or companies and never has.
AI maturity is a critical determinant of success in almost every industry. A chasm is opening between early and late adopters. Leading organizations use traditional AI to find solutions to business-critical problems, but their challenges cannot be solved with standard correlation-based machine learning. Forward-thinking organizations are turning to Causal AI.
The organization doesn’t use artificial intelligence to make decisions. Most organizations are stuck at L0. AI is confined to data science experiments and has no impact on real decision-making. The limitations of standard machine learning technology are to blame.
Core problems include a lack of adaptability to real-world dynamics, and a lack of explainability. Data scientists may attempt to implement “post hoc explainability” methods, but these methods do not produce actionable insights.
There are many other limitations of standard machine learning (see L1). However, these two mean that business decision-makers do not trust AI systems sufficiently to let them out of the lab. Read up on the shortfalls of current state-of-the-art AI here.
Organizations stuck at L0 are wasting resources on AI investment which has zero impact.