Practical deep-dives into causal inference, including difference-in-difference, fixed effects, doubly robust estimator, instrumental variable and regression discontinuity design, — the core topics every product data scientists needs to master.
Learn how to apply Two-Way Fixed Effects (TWFE) models to estimate interaction effects between product color and search queries. We cover the bias pitfalls of TWFE under staggered adoption and practical strategies for robust causal estimation in e-commerce settings.
Istio config tweaks and node pool changes shift latency without anyone noticing — but a 100ms delay has been shown to move revenue by 0.6%. A cheap before/after check to run before building a full infra experiment pipeline.
The same GIF-thumbnail experiment from the A/B Testing section, reconstructed as a textbook instrumental-variables problem — relevance, exclusion restriction, the Wald estimator, and exactly which population a LATE estimate describes.