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Experimentation & Causal Inference guides
How to choose an attribution model Rule-based, data-driven, MMM and incrementality testing answer different questions — pick by what decision the answer has to support. How to choose an experimentation and feature-flag platform Pick by where your experiment data comes from — flag telemetry or your own warehouse — and which statistical engine your team trusts, not feature count. How to evaluate a public program Logic models, the counterfactual problem, and how RCTs and quasi-experimental methods answer "did this actually work" honestly. How to read an A/B test result Significance, power, confidence intervals, sample ratio mismatch and peeking — what a dashboard's green checkmark does and does not tell you. How to run marketing mix modeling What data an MMM needs, the build-vs-buy choice between open-source libraries and managed vendors, and how to validate the model with real experiments. How to set up an experimentation program From your first A/B test to a durable program — the plumbing, the statistics rules, the review ritual, and the tools that support each stage.