Glossary
Causal Analysis
Going beyond correlation to understand causality. Not 'Who knows whom?' but 'Who controls whom?'
Definition
Causal Analysis is the discipline of understanding why things happen-not just that they co-occur. Correlation tells you that A and B move together; causality tells you that A causes B. Sentient OS applies causal analysis through vector-space modeling: understanding influence (who initiates opinions vs. who amplifies), control (who drives decisions), and driver attribution (what actually moves conversion). The Conversion Modeling module uses multi-factor causal modeling-price sensitivity, engagement quality, match quality-rather than last-click attribution. Causal analysis enables deterministic execution: when you know why something works, you can replicate it. The platform answers 'Who controls whom?' in markets, audiences, and campaigns-the foundation of informational superiority.
Why It Matters
Causal analysis is Sentient's differentiator. We don't report correlations; we model causality. That's what enables deterministic execution and prescriptive intelligence.
Related Pages
Related Terms
Attribution Modeling
Assigning credit for conversions. Sentient uses multi-factor modeling beyond basic attribution.
Deterministic Execution
Moving from probabilistic guessing to mathematically precise, causal decision-making.
Influence Detection
Recognizing who initiates opinions vs who merely amplifies. Finding true decision-makers, not just loud voices.
Vector Spaces
High-dimensional mathematical spaces where actors, products, campaigns become 'Persona Vectors.' Mathematics instead of databases.
Predictive Analytics
Forecasting future outcomes using historical patterns. Sentient goes beyond to prescriptive/deterministic.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.