A fundamental need in various industries is to find underlying causal relations and use them to improve the business outlook of companies. All companies bar none would like to understand why their sales are declining, what leads customers to buy, how to improve employee motivation, where to allocate resources and what is the best competitive strategy. The knowledge of causal relations is essential in achieving these goals. However, in most analyses, scientists draw inferences from correlation. However, correlation is not causation, and such correlative inferences can often be disastrously incorrect. This talk covers the following: - An insight into the data science of Causal Discovery - How to generate causal hypotheses to determine root causes of operational situations - How to conduct “natural experiments” to validate these hypotheses from a historical dataset - Real-world case studies of the successful application of causal discovery in industry About the speaker: Vijay Nadkarni is Vice President Artificial Intelligence at Tech Mahindra, responsible for AI technology and customer engagements across multiple verticals including automotive, manufacturing, aerospace, healthcare, retail, semiconductor and others. Previously, Vijay was VP and global head of AI at Visteon, responsible for AI across its product lines, notably autonomous driving and infotainment. A veteran of Silicon Valley, Vijay has co-founded multiple startups in AI and cloud computing of which Veraz Networks, a VoIP company, had a NASDAQ IPO, and another, Wellcore, was acquired.

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17:00 - 18:00 hs GMT+1

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Business Intelligence and Analytics
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