Causal Inference for Data Scientists: Techniques & Tools

The session consists of three parts. In the first part, we will provide a broad overview of Causal AI and explore how companies are leveraging causal inference in practice. The second part will focus on essential causal inference techniques such as instrumental variables, propensity scoring etc. In the final part, we will discuss popular causal inference libraries like DoWhy CausalPy. etc We will also go through practical demo showcasing the application of causal techniques.

Key Takeaways:

• Gain a broad overview of Causal AI and discover how companies are leveraging causal inference in real-world applications.
• Learn essential causal inference techniques, including instrumental variables and propensity scoring.
• Explore popular causal inference libraries like DoWhy and CausalPy for practical implementation.
• Witness a hands-on demo showcasing the application of causal techniques to solve real-world problems.

Srimugunthan brings over 12 years of experience in data analytics, specializing in prediction models, recommender systems, and more. With a background in computer science and an Executive MBA in product management, he's well-equipped to guide us through the complexities of bias in ML models. You can reach him on LinkedIn.