{"id":11103,"date":"2026-08-10T10:27:47","date_gmt":"2026-08-10T10:27:47","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-causal-inference-for-data-science-2025-1\/"},"modified":"2026-08-10T10:27:47","modified_gmt":"2026-08-10T10:27:47","slug":"oreilly-causal-inference-for-data-science-2025-1","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-causal-inference-for-data-science-2025-1\/","title":{"rendered":"Oreilly \u2013 Causal Inference for Data Science 2025-1"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Causal Inference for Data Science. This course teaches participants how to combine statistical methods and machine learning to identify causal relationships between events and measure their effects in order to improve outcomes. In the world of data science, understanding the cause of events is key because it allows for intelligent intervention rather than mere prediction. When conducting controlled experiments such as A\/B testing is difficult or expensive, this course teaches alternative techniques to discover causal relationships using existing data. Topics include modeling reality with causal graphs, estimating effects with statistical methods, choosing the most appropriate tool (A\/B testing, machine learning, or causal inference), and evaluating the assumptions and limitations of the analysis. You will also learn how to analyze historical data, understand customer behavior, and facilitate effective decision-making by identifying key drivers. This course will provide you with the tools necessary to build models that not only predict patterns but also reveal the underlying causes.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n