Causal Decision-Making Loop
by Ramesh Johari • Professor at Stanford University / Data Science Advisor at Stanford University (Advisor to Airbnb, Uber, Stitch Fix, etc.)
Ramesh Johari is a professor at Stanford University specializing in data science, game theory, and the design of online marketplaces. He has served as an advisor to major tech companies like Airbnb, Uber, and Upwork, helping them solve complex problems related to matching, pricing, and experimentation.
🎙️ Episode Context
Ramesh Johari dives deep into the science of building thriving marketplaces, arguing that marketplaces primarily sell the reduction of transaction friction rather than just goods. He explores the crucial distinction between predictive machine learning and causal decision-making, the nuances of designing fair rating systems, and why founders should focus on liquidity before platform dynamics.
Problem It Solves
Data teams often build predictive models (e.g., LTV prediction) that find correlations but fail to guide actual business decisions (e.g., who to target with coupons).
Framework Overview
Shift focus from 'Prediction' (Machine Learning/Correlation) to 'Decision Making' (Causal Inference). The goal is not to predict the future, but to understand how a specific intervention changes the future.
🧠 Framework Structure
Prediction ≠ Decision: Recognizing th...
Experiment for Learning: Move beyond ...
Bayesian Updates: Incorporate prior b...
When to Use
When optimizing pricing, ranking algorithms, or marketing spend where correlation might be mistaken for causation.
Common Mistakes
Sending promotions to high LTV customers who would have purchased regardless, simply because a model predicted they were 'good' users.
Real World Example
A marketing manager running an unauthorized holdout group to prove the incremental value of their ad spend, acknowledging that learning the true lift costs money (lost revenue from the holdout).
Prediction is inherently about correlation. But when we ask people to make decisions, we're asking them to think about causation.
— Ramesh Johari