Ivan Gorban’s Post

🪜 The Ladder of Causation: From Observation to Action Post 3 in my Causal Inference Series If correlation alone can’t guide smart decisions, what can? Judea Pearl introduced a great mental model to think clearly about causation: the Ladder of Causation. It has three main rungs: 1. Association (Seeing): "What does the data tell us?" Example: Ice cream sales correlate with shark attacks - this observational data shows patterns, not causes. 2. Intervention (Doing): "What happens if we actively change something?" Example: If we launch a targeted marketing campaign, how many new customers will it actually attract? 3. Counterfactuals (Imagining): "What if we had done something differently?" Example: Would a customer still have churned if we’d offered a discount earlier? 🔑 Why does this ladder matter so much? Climbing it shifts our thinking from merely observing ("this happens") to confidently acting ("doing this will cause that"). The higher we climb, the stronger - and more reliable - our decisions become. Think about how a child learns. First, they notice patterns - certain sounds signal food or play. Then, they actively experiment - dropping toys and watching them break. Eventually, they imagine scenarios that haven’t even occurred yet. This natural learning progression mirrors the ladder perfectly. That’s precisely why Randomized Controlled Trials (RCTs), also well-known as A/B tests, are so powerful. Randomly assigning people to treatment or control groups ensures your actions truly cause observed differences. Randomization effectively "breaks" confounding factors, preventing biases from distorting your results. We’ll unpack exactly how this works when we explore causal graphs. But randomization isn’t always practical or even possible. When not, we use other methods to handle confounding and make trustworthy causal claims. In upcoming posts, we’ll cover confounders, mediators, colliders, and other essential tools to climb this ladder effectively. 💬 I’d love to hear about your experiences or challenges with causal thinking - share in the comments! #CausalInference #DataScience #MachineLearning