Summary

How do causes lead to effects? Can you associate the cause leading to the observed effect? Big Data opens the doors for us to be able to answer questions such as this, but before we are able to do so, we must go beyond classical probability theory and dive into the field of Causal Inference.

In this course, we will explore the three steps in the ladder of causation: 1. Association 2. Intervention 3. Counterfactuals with simple rules and techniques to move up the ladder from simple correlational studies to fully causal analyses. We will cover the fundamentals of this powerful set of techniques allowing us to answer practical causal questions such as “Does A cause B?” and “If I change A how does that impact B?”

Program

Approaches to Causality
- Probability Theory
- Simpsons Paradox
- A/B Testing
- Granger Causality
- Graphical Models
- The Ladder of Causality
Properties of Graphical models
- Chains
- Forks
- Colliders
- d-separation
Interventions
- Interventions
- Back-door criterion
- Front-door criterion
- Mediation
Counterfactuals
- The fundamental laws of counterfactuals
- Graphical representation
- Practical Applications
Connections to Machine Learning
- Structure Identifiability
- Semi-Supervised learning
- Applications to time-series