{"id":7457,"date":"2026-08-10T09:44:22","date_gmt":"2026-08-10T09:44:22","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-causal-ai-an-extensive-introduction-2024-8\/"},"modified":"2026-08-10T09:44:22","modified_gmt":"2026-08-10T09:44:22","slug":"udemy-causal-ai-an-extensive-introduction-2024-8","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-causal-ai-an-extensive-introduction-2024-8\/","title":{"rendered":"Udemy \u2013 Causal AI: An Extensive Introduction 2024-8"},"content":{"rendered":"
\n

Descriptions<\/h2>\n

Causal AI: An Extensive Introduction, More<\/span> and<\/span> more<\/span> people are starting to realise that correlation-focused models are not enough to answer our most important business questions. Business decision-making is all about understanding the effect different decisions have on outcomes, and choosing the best option. We can\u2019t understand the effect decisions have on outcomes with just correlations; we must understand cause and effect. Unfortunately<\/span>,<\/span> there is a huge gap of knowledge in causal techniques among people working in the data & statistics industry. This means that causal problems are often approached with correlation-focused models, which results in sub-optimal or even poor solutions.<\/p>\n

In<\/span> recent<\/span> years<\/span>, the field of Causality has evolved significantly, particularly due to the work of Judea Pearl. Judea Pearl has created a framework that provides clear and general methods we can use to understand causality and estimate causal effects using observational data. Combining his work with advances in AI has given rise to the field of Causal Artificial Intelligence. Causal<\/span> AI<\/span> is<\/span> all<\/span> about using AI models to estimate causal effects (using observational data). Generally, businesses rely only on experimentation methods like Randomized Controlled Trials (RCTs) and A\/B tests to determine causal effects. Causal AI now adds to this by offering tools to estimate causal effects using observational data, which is more commonly available in business settings. This is particularly valuable when experimentation is not feasible or practical, making it a powerful tool for businesses looking to use their existing data for decision-making.<\/div>\n

What you\u2019ll learn<\/h3>\n