Causal AI models represent how systems work, while today’s ML models just track patterns in data. Causal AI models allow users to evaluate the impact of actions ahead of time and answer “why”-questions, going far beyond standard predictive analytics. And Causal AI models are intrinsically explainable, whereas conventional AI is a black box technology. causaLab makes Causal AI models accessible to everyone.
Conventional ML just identifies correlations — but correlation is not causation
From a causal graph to a predictive model
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.”
We use causaLens to autonomously discover alpha in our data. We would have not been able to discover these valuable causal signals without it.”
Causal AI enables us to identify significant and unexpected changes in key factors associated with the FX markets, enabling quick reactions to market conditions and enhancing investing strategies”
The novel Causal AI techniques available on the causaLens Platform have facilitated our joint effort of discovering valuable profitable trading strategies”
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.
Transparency and explainability of AI models requires an understanding of causality—an inherent advantage of the causaLens platform”
causaLens’ causality-based techniques & automation of quantitative workflows help us discover more orthogonal signals faster while discarding spurious correlations”
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