AI Ecosystem CourseBy Queensly Austin
Module 17

Decisions, Forecasting, Optimization, And Causality

Introduction Available · Lessons In Development

Introduction

What will happen? What should we do? What would change because we acted? Those are three different questions. Prediction, decision-making, and causal analysis address them in different ways. A model can generate an explanation or proposal without resolving any of them. This module follows the steps between an output and a defensible action.

We will use small inventory, customer-work, and AI-adoption examples to explore probability, expected value, thresholds, forecasts, constraints, optimization, counterfactuals, confounding, experiments, and causal assumptions. You can calculate the examples by hand and see which evidence each question needs.

Revisit classification metrics in Module 1, evaluation in Module 16, and units and cost boundaries in Module 8. Work through decisions, then causality, then forecasting and optimization. Your decision memo should state an action rule, a causal question where relevant, and how you would test whether the action creates value.

Planned lessons

  1. Four Questions That Need Different Answers
  2. Probability And Expected Value
  3. Decision Thresholds Follow Consequences
  4. Base Rates, Bayes, And Calibration
  5. Interventions And Counterfactuals
  6. Confounding And Selection
  7. A Task-Mix Example That Reverses The Story
  8. Causal Graphs, Identification, And Assumptions
  9. Randomized Experiments And The Assignment Unit
  10. Experimental Analysis And Misleading Significance
  11. Observational Causal Methods And Their Limits
  12. Forecasts Have Horizons And Baselines
  13. Distributions, Scenarios, And Asymmetric Loss
  14. Optimization: Objectives, Choices, And Constraints
  15. Sequential Decisions, Bandits, And Reinforcement Learning
  16. Human Authority And A Decision Record