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.

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.