The Hitchhiker's Guide to Your Users. Personalization Through Causal ML
Did you ask yourself why some products have more conversion? Or why do some users select only a kind of product? In this talk, some of these questions are going to be treated and explained using causal inference. It will explain how to estimate the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on the model form. Causal ML can be used to find the group of people that have a favorable response for a given metric such as engagement or sales. This is an example of campaign targeting optimization, but also it can be used to personalize the engagement with our clients. You can use it to estimate the effect for each customer for an optimal personalized recommendation system.
Head of AI at Plain Concepts
Head of AI at Plain Concepts. Excited about new technologies, robotics, and artificial intelligence. I am so passionate about it that I did a PhD at Technical University of Madrid in these subjects. So, I have been working in this field for more than a decade in both academia and business fields. Among the projects I have been working on, I have been developing neural networks for signal analysis, image processing, natural language processing, chatbots, virtual assistants, etc. I have also been in a product company such as Cabify where I have been in charge of projects to create an impact in the marketplace, through automation of tasks, creation of different models for the monitoring and improvement of its users, as well as their implementation production. Since rejoining Plain Concepts I have been working on coordinating a data and AI team to deliver end2end solutions to our clients. Besides the software, I really enjoy cycling, listening to music and reading.
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