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. Danial interest lies in the area of predictive & causal modelling theory and its application. In his current adventure at ABN AMRO bank, he contributes to e-commerce data-driven solutions to improves clients experience, engagement, and satisfaction.
Sessions
Applying the tools and techniques from causal effect estimation in a practical setting can be challenging. Randomized experiments, as the golden standard method for effect estimation, are often not practical. Alternative solutions use observational (non-experimental) data, while they introduce their own challenges, which will be addressed in this talk. These challenges are often not elaborately discussed in text books and can be summarized as follows: 1) samples from treatment group are not available all at once and could become available throughout time (online data stream), 2) appropriate control groups are not immediately available for comparison with the treatment group, and 3) the outcomes of choice in causal effect estimation should be in line with the business questions and accepted KPI’s in the domain.
This self-contained talk targets generic data scientists by presenting the theory of causal effect estimation in a simplified and visual (little-math) fashion. In addition, technical & business requirements, lessons learned from e-commerce & banking, and results are shared when it comes to applying causal effect estimation in practice.