𝗖𝗮𝘂𝘀𝗮𝗹 𝗔𝗜 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱: 𝗗𝗮𝘆 𝟰 - 𝗜𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝘀 So far, we’ve tackled the do-operator and the trio of confounders, colliders, and mediators. Today, let’s dive into 𝗶𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝘀, the approach that lets us test causal effects via "what if" simulations. Most statistical and ML models observe the world as it is: "Sales went up when we ran ads". Not bad, but what would happen if we could change something in the process? Interventions flip the script, letting us ask, "What if we tweak X? How does Y respond?". This is about simulating actions, not just spotting patterns. 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 Think of a retailer: Sales rise with discounts. Correlation says they’re linked, but did the discount cause it? An intervention lets us force a change: "What if we set discounts to 20% across all stores, holding other factors like seasonality steady?". This isolates the discount’s causal effect, cutting through the noise. In maths terms, we shift from observing the conditional 𝗣(sales|discount) to the causal 𝗣(sales|𝗱𝗼(discount = 20%)). By the way, this is where our 𝗱𝗼-𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿 from day 2 takes place! 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 Imagine a health app: Users who log workouts sleep better. Correlation? Sure. But what if we intervene: "What if we nudge half our users to log workouts daily?". A causal model might show sleep improves by 10%. Or, it might not improve at all and the better sleep might be the effect of something else. That’s a decision-shaping insight! Can you spot a potential confounder here? 𝗪𝗵𝘆 𝗜𝘁’𝘀 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 • Simulation: Test scenarios without real-world costs or risks • Precision: Pinpoints what drives outcomes, not just what tags along • Strategy: Turns "this happened" into "this will happen if we act" 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: 𝗜𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 "𝘄𝗵𝗮𝘁 𝗶𝗳" 𝘁𝗼𝗼𝗹 𝗼𝗳 𝗖𝗮𝘂𝘀𝗮𝗹 𝗔𝗜, 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗼𝗻𝗲 "𝗱𝗼" 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲. Ever ran a "what if" scenario that changed your perspective? Drop your thoughts below! #CausalAI #DataScience #Causality #ArtificialIntelligence #AIExplained #Interventions
