Glossary
Causal Analysis
Going beyond correlation to understand what actually drives outcomes. Not just 'who knows whom' but 'who influences whom and how.'
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 guesswork to precise, cause-based decision-making - decisions grounded in what actually drives outcomes.
Influence Detection
Recognizing who initiates opinions vs who merely amplifies. Finding true decision-makers, not just loud voices.
Vector Spaces
A mathematical space where people, products, and content are represented so that 'closeness' means compatibility. The foundation for precise matching. Mathematics instead of databases.
Predictive Analytics
Forecasting future outcomes from historical patterns. Sentient goes further: it also recommends and executes what to do about them.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.