Why Causal AI?
Through the analysis of your numerical, tabular or time-series data, causal AI enables reliable and accurate “what-if” exploration of countless business scenarios to allow you to determine the optimal course of action.
Causal AI is already helping to …
Move beyond predictions
AI has always focused on predictive questions: “What will happen next?”. Causal AI changes this.
Causal AI can answer predictive questions but also unlocks interventional and counterfactual ones, such as:
- What actions should I take to maximize retention?
- What if I increased my marketing spend?
- What is the root cause of machine failures?
Causal AI unlocks the ability to accurately evaluate countless possible futures to determine the optimum path forward. This is true decision intelligence.
Trust AI Decisions
Predictive AI models are uninterpretable black boxes. Methods like LIME and SHAP provide limited explainability and do not provide guarantees of model behavior on unseen inputs. They can only tell you the features which are correlated with predictions but are not necessarily causal drivers.
Meanwhile, Causal AI models are interpretable white boxes – showing clearly the causal relationships between features. Causal AI models provide clear guarantees of model outputs making them robust to outliers.
This results in Causal AI models being highly trustworthy and capable of supporting your mission critical use cases.
Causal AI and AI Agents
Causal Reasoning
Enterprise-grade causal AI ecosystem that enables reliable and accurate “what-if” exploration of countless business scenarios to determine the optimal course of action.
Agentic AI
AI agents that enable Data Science teams to rapidly move from data cleaning through data analysis to building underlying models that can drive decision-making.
causaLens combines Causal AI with Agents to help you build 10x faster
Transform your enterprise with AI agents infused with causal reasoning and capable of solving complex, quantitative problems. decisionOS accelerates you through the causal workflow with its powerful agents.
The Causal Workflow
Causal Discovery
Causal discovery is the process of combining algorithms and domain expertise to discover a causal graph from observational data.
Causal graphs model the underlying data generating process rather than simple associations between variables.
decisionOS contains a full suite of the best-in class Causal Discovery algorithms that allow you to estimate causal graphs in a wide variety of settings.
Causal Modeling
Once causal relationships have been inferred you train a structural causal model.
In a structural causal model the relationships between variables represent causal effects, a representation of the underlying mechanism by which the system operates.
Decision Intelligence
A structural causal model allows us to go beyond pure predictions and measure the effect of interventions and counterfactuals. Using these results allows us to unlock a new class of decision intelligence.
- Optimization: Provide the optimal interventions to achieve an objective.
- Causal Effect Estimation: Quantify how interventions impact different groups within your data.
- Root Cause Analysis: Identify the causal drivers of anomalous events.
- Causal Fairness: Determine how discrimination can occur within your model as you perform interventions and actions.
Ways to get started
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Read our white papers, case studies, and research to gain new understanding in the world of causality.
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