measurement-experimentation-opslisted

# Measurement & Experimentation Ops The other skills act on measured differences; this decides whether a difference is real or noise before they do. ## Pick the testing mode by the decision at stake Three modes, different evidence bars — match to the cost of being wrong: 1. **Causal**: estimates incrementality (no design *proves* causality without assumptions). Two sub-modes — a randomized experiment (one treatment vs a non-overlapping control/holdout, pre-sized) is the strongest; quasi-experimental causal estimation (GeoLift synthetic control, pre/post) is the fallback when you can't randomize. Use for expensive, hard-to-reverse bets: offer, funnel, landing page, "does this channel even lift sales." Cost: volume + discipline + often a Meta rep. 2. **Screening** (directional): many concepts in one ad set / parallel ABO cells; delivery is UNEQUAL by design, so a "winner" is a hypothesis, not a proof. Use for high-throughput creative hunting where being fast beats being certain. Never present a screen result as validated. 3. **Infrastructure** (isolate infra variance): hold the CREATIVE fixed, vary one infra axis (domain / proxy cluster / account batch) across a balanced set to attribute delivery/ban/CPM differences to infra, not creative. The grey inversion of a normal test — see meta-grey-ops/06. ## Feasibility gate (grey reality — check BEFORE promising a clean test) Causal measurement often isn't available on grey/small-account buys