What problem does it solve? Founders often mistake correlated metrics for causal drivers, leading to wasted spend on initiatives that do not actually move the target outcome. This Skill structures causal reasoning so decisions are backed by evidence, explicit assumptions, and quantified uncertainty rather than intuition. ## Core Features & Use Cases - Causal Design Selection: Defines the intervention and counterfactual, draws causal assumptions, identifies bias, and chooses an appropriate experiment or quasi-experiment design. - Risk-Adjusted Decision Ranking: Scores options with risk-adjusted value, confidence weighting, and hard-constraint checks, then recommends the smallest viable action portfolio. - Monitoring and Learning Loop: Sets leading indicators, stop/scale conditions, and review dates so expected versus actual results are compared and assumptions updated. - Use Case: A founder asks whether a pricing change caused a churn spike. The Skill separates correlation from causation, estimates the effect with robustness tests, and recommends a reversible test before committing further resources. ## Quick Start Use causal decision analysis to determine whether our recent marketing campaign actually caused the signup increase and recommend next steps within our budget limits.
