The article explores the intersection of philosophy and data science, focusing on causality. It delves into different philosophical theories of causality, such as deterministic vs probabilistic causality, regularity theory, process theory, and counterfactual causation. The author emphasizes the importance of understanding causality in data science to provide valuable recommendations.
“`html
Philosophy and Data Science — Thinking Deeply about Data
Part 3: Causality
My hope is that by the end of this article you will have a good understanding of how philosophical thinking around causation applies to your work as a data scientist. Ideally you will have a deeper philosophical perspective to give context to your work!
Introduction
I love how many philosophical topics take a seemingly obvious concept, like causality, and make you realize it is not as simple as you think. This is the third part in a multi-part series about philosophy and data science.
Causality’s Unobservability
David Hume, a famous skeptic, made the astute observation that we cannot observe causality directly with our senses. This is the primary challenge of causality, we have to infer it from our observations.
Deterministic vs. probabilistic causality
Deterministic causality states that causal relationships have no elements of randomness in them while probabilistic causality proposes that there is some randomness in the causal relationship.
Regularity theory of causality
The regularity theory defines causation by the regular sequencing of events. It simplifies identifying ‘causation’ but may not offer practical knowledge.
Process theory of causality
Process theory seeks to understand the reason behind causation and looks to explain the relationships between events.
Counterfactual Causation
The counterfactual causation approach establishes causal relationships between events by asking ‘What would’ve happened had things been different?’
Bringing it all together
The philosophy of causality gives data scientists a lot of useful perspectives on how causality can be understood and used to add data-driven value.
Discover how AI can redefine your way of work
If you want to evolve your company with AI, stay competitive, and use for your advantage Philosophy and data science — Thinking deeply about data.
Identify Automation Opportunities:
Locate key customer interaction points that can benefit from AI.
Define KPIs:
Ensure your AI endeavors have measurable impacts on business outcomes.
Select an AI Solution:
Choose tools that align with your needs and provide customization.
Implement Gradually:
Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice, connect with us at hello@itinai.com.
Spotlight on a Practical AI Solution: Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.
“`
