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causality

Learn Why Do we need Causality in Data Science?

Tuesday, January 28 at 9 p.m. IST

This is a Webinar explaining why we need causal inference(Causality) in data science and machine learning. Causal inference brings a new fresh set of tools and perspectives that let us deal with old problems.

First off, designing and running experiments (typically with A/B testing) is always better than using causal inference techniques: you don’t need to model how data is generated. If you can do that, go for it!

There are Three Main Sources of influence in Casual inference: Computer Science, Statistics, and epidemiology and econometrics.

causality

In this webinar, we will cover:

  • Need for Casual Inference – moving from Prediction to Decision.
  • Common Questions in Decision Making scenario – Will it Work? Why did it Work? What should we do? What are the overall effects?
  • Simpson’s Paradox
  • Case Studies.
  • Application: A/B Testing, Marketing Mix Models, Business Decision Making.

Register yourself for the Webinar now! Stay up to date with the Data Science progression in the industry.

Prakhar Gupta

Presented By

Prakhar Gupta

Founder of Adorithm | Measurement Expert | ML & AI Evangelist

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