Nazli M. Alagoz
I am a quantitative researcher and data scientist with a strong background in marketing, economics, and econometrics. My focus is on using data-driven approaches to tackle complex business challenges, uncover valuable insights, and drive impactful decisions. As a Ph.D. candidate in quantitative marketing, I specialize in causal inference, machine learning, and experimental design to address cutting-edge research questions.
Sessions
The Difference-in-Differences (DiD) methodology is a popular causal inference method utilized by leading tech firms such as Microsoft Research, LinkedIn, Meta, and Uber. Yet recent studies suggest that traditional DiD methods may have significant limitations when treatment timings differ. An effective alternative is the implementation of the staggered DiD design. We exemplify this by investigating an interesting question in the music industry: Does featuring a song in TV shows influence its popularity, and are there specific factors that could moderate this impact?