Danial Senejohnny
Danial is a data scientist & analytics translator with a PhD in applied mathematics (systems & control). In his career, he has experienced different sectors, i.e. manufacturing, cybersecurity, healthcare, and finance. In his current adventure at ABN AMRO, he contributes to personalized solutions that improves clients experience and satisfaction.
Session
Causal inference techniques are crucial to understanding the impact of actions on outcomes. This talk shares lessons learned from applying these techniques in real-world scenarios where standard methods do not immediately apply. Our key question is: What is the causal impact of wealth planning services on a network of individual’s investments and securities? We'll examine the challenges posed by practical constraints and show how to deal with them before applying standard approaches like staggered difference-in-difference.
This self-contained talk is prepared for general data scientists who want to add causal inference techniques to their toolbox and learn from real-world data challenges.