PyData Berlin
Causal Inference in Network Structures: Lessons learned From Financial Services
Talk
Abstract
*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.*
Prerequisites
This talk is self-contained
Description
Wealth planning is a service offered by financial institutions. The advice helps clients grow their wealth through investing. This talk focuses on measuring the true impact of these services on a network of individual’s investments and securities. However, measuring this impact presents several practical challenges, which will be tackled in this talk:
1) Controlled experiments are often impossible in practice, leaving only observational data available.
2) Defining robust control groups is challenging when treatments are administered to individuals in relationship graphs at different times.
3) Analysis must account for multiple outcomes with different modalities—securities (time-series) and investing (binary).
4) The parallel-trend assumption doesn't immediately hold.
5) Market trends confounding effect on outcome needs to be corrected.
Speaker
Danial Senejohnny
Data Scientists & Analytics Translator
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.