Insights, for ab testing, causal inference and data analysis
Experiment design, sequential testing, and statistical methodology for running reliable A/B tests.
Field notes from real experiment reviews — the ambiguous rollout calls, the metric traps, and the reasoning that actually resolved them.
Econometric methods and causal frameworks for estimating treatment effects when a clean randomized experiment isn't available, or isn't enough on its own.