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Hebner Advisories

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Today’s AI is quite impressive. But wait until one of the most important MISSING INGREDIENTS gets added into the mix. This ingredient, CAUSALITY, is what will make AI a truly indispensable partner in business.  With an understanding of cause & effect, AI can help you reason, problem-solve, and make better decisions. 

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Concept

No matter how sophisticated a predictive model is, it only establishes a correlation between a behavior or event and an outcome. However, that is very different from saying that the outcome happened because of the behavior or event. Correlation doesn't imply causation. There can be correlation but not causation. And while causation implies correlation, its influence may be so minor its irrelevant. Equating them risks creating an incubator for hallucinations and bias,

Causality will enable businesses to do more than create predictions, generate content, identify patterns, and isolate anomalies. They’ll also be able to play out countless scenarios to understand the consequences of various actions, explain the causal drivers of their business, and analytically problem-solve. They’ll know WHAT to do, HOW to do it, and WHY certain actions are better than others to prescriptively shape future outcomes.  

Simply put, humans are causal by nature, so AI must also be causal by nature. LLMs are similar to the limbic brain, which drives instinctive actions based on memories, making them good at automating tasks. Casual AI plays the role of the cerebral cortex, which encodes explicit memories into tacit know-how, and the neocortex, which drives higher-order reasoning such as problem-solving, planning, and decision-making.

Benefits

Causality

Root Cause

Causality

Identify precise cause-and-effect relationships that help you understand why things happen 

What If

Root Cause

Causality

Play out countless scenarios to understand, risk-free, how various  interventions may impact outcomes

Root Cause

Root Cause

Prescription

Understand what variables influence your metrics, ranked by impact and confidence levels

Prescription

Prescription

Prescription

Generate step-by-step recommendations that guide you to desired outcomes 

Intuition

Prescription

Confounders

Capture expertise, domain knowledge, tacit know-how, and known conditions in AI models

Confounders

Prescription

Confounders

Identify misleading, irrelevant, biased, or previously concealed influences affect outcomes

Explaination

Explaination

Explaination

Open up AI "black boxes" to understand how and why certain outcomes were deduced

Simplicity

Explaination

Explaination

Create smaller yet more sophisticated AI models that uses only the data that matters

Agentics

Explaination

Agentics

Enable AI agents that interact with each other based on cause-and-effect relationships

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In Action

While GenAI has sparked excitement, Causal AI might offer much greater potential by being able to unravel the intricate web of cause-and-effect relationships across a business.

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Instana can now use causal AI to identify root causes of IT faults and showcase how and why certain entities were identified as probable cause of the identified problematic entities. 

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Using the ML framework, we can test and identify the various aspects of promotional artwork to give Netflix members a better and more personalized experience.. 

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Dive Deeper

An excellent book for tech-savvy business leaders, data scientists and IT leaders

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A great book to better understand the science of why and  the study of cause and effect

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