About this blog
Most statistics education stops at textbooks. This blog bridges the gap — translating causal inference, A/B testing methodology, and econometric theory into the kind of reasoning that holds up when a PM asks "is this experiment actually working?"
Who writes this
I'm a Senior Data Analyst at a e-commerce unicorn, working on experimentation, recommendation, and search analytics — close enough to product and engineering that the role often looks more like Product Data Scientist in practice. Before that, I finished a PhD in Economics, where I spent most of my time thinking about causal identification — instruments, difference-in-differences, and whether any of it would survive contact with real data.
The short answer: some of it does. A lot of it needs translation. That translation is what this blog is about.
Academia
PhD, Economics
Applied Microeconomics — causal inference, IV, DiD. Job market paper on social network formation.
Research
Research Institute
Applied econometrics on government policy.
Startup
Early-stage Startup
Applied A/B testing frameworks, surrogate index LTV models to estimate the impact early
Now
E-commerce Unicorn
Senior Data Analyst working like a Product Data Scientist — sequential ab testing, ad attribution vs. incrementality, and causal inference including synthetic difference in difference.
What you'll find here
These posts start from a real operational problem, not a method looking for an application. Teams want to end experiments faster — but peeking at results inflates false positives. They want protection from a bad launch — but waiting for a fixed window leaves guardrail metrics blind until the very end. Group Sequential Testing is a good example: it isn't here because it's mathematically interesting, it's here because it's the correct fix for exactly those two problems. Every post on this blog works the same way — start from the pain point, then bring in only as much methodology as it takes to resolve it safely.