Nazlı Alagöz
I’m a data scientist at Booking.com, where I work on applying causal inference and machine learning to solve business problems and support decision-making. I have a Ph.D. in quantitative marketing, with a background in econometrics and experimental design. My work sits at the intersection of economics and data science, and I enjoy using data to uncover actionable insights and understand real-world behavior.
Session
This talk dives into the challenge of measuring the causal impact of app installs on customer loyalty and value, a question at the heart of data-driven marketing. While randomized controlled trials are the gold standard, they’re rarely feasible in this context. Instead, we’ll explore how observational causal inference methods can be thoughtfully applied to estimate incremental value with careful consideration of confounding, selection, and measurement biases.
This session is designed for data scientists, marketing analysts, and applied researchers with a working knowledge of statistics and causal inference concepts. We’ll keep the tone practical and informative, focusing on real-world challenges and solutions rather than heavy mathematical derivations.
Attendees will learn:
* How to design robust observational studies for business impact
* Strategies for covariate selection and bias mitigation
* The use of multiple statistical and design-based causal inference approaches
* Methods for validating and refuting causal claims in the absence of true randomization
We’ll share actionable insights, code snippets, and a GitHub repository with example workflows so you can apply these techniques in your own organization. By the end of the talk, you’ll be equipped to design more transparent and credible causal studies-and make better decisions about where to invest your marketing dollars.
Requirements: A basic understanding of causal inference and Python is recommended. Materials and relevant links will be shared during the session