Causal Effects | An introduction
🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/BOPOX_mTS0g This is the first video in a series on causal effects. Here I introduce the Potential Outcomes Framework and use it to formulate 3 different types of causal effects. In future videos, I discuss how to compute causal effects from observational data. Series Playlist: • Causality 📰 Read more: https://medium.com/towards-data-scien... Resources: An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter C. Austin Counterfactuals and Causal Inference: Methods and Principles for Social Research by Stephen L. Morgan & Christopher Winship An Introduction to Causal Inference by Judea Pearl Introduction - 0:00 Causal Effects - 0:24 3 Types of Variables - 1:01 Potential Outcomes Framework - 1:50 3 Types of Causal Effects - 2:28 1) Individual Treatment Effect (ITE) - 2:38 2) Average Treatment Effect (ATE) - 4:01 2.1) ATE in RCTs - 5:24 3) Average Treatment Effect of Treated/Controls (ATT/ATC) - 6:56 Practical Questions - 9:07
Causal Effects via Propensity Scores | Introduction & Python Code
Causal Effects via the Do-operator | Overview & Example
Causal Discovery | Inferring causality from observational data
Causal Effects via DAGs | How to Handle Unobserved Confounders
Judea Pearl: Do(x) Operator and Do-Calculus | AI Podcast Clips
The Reasoning Test Psychologists Still Can't Explain
The 11 Minute Introduction to Mastering Potential Outcome Notation
Causal Inference | Answering causal questions
14. Causal Inference, Part 1
Propensity scores: Everything you need to know in 5min
Correlation vs Causation Explained: Why Patterns Can Mislead Us
An introduction to Causal Inference with Python – making accurate estimates of cause and effect from
How to Build an LLM from Scratch | An Overview
Causality: An Introduction | How (naive) statistics can fail us
Statistical Learning: 11.1 Introduction to Survival Data and Censoring
Causal Inference - EXPLAINED!
Causality (and the difference to correlation) simply explained
Watch this if everything feels too much (gentle comfort for tired women)
Propensity score matching: an introduction
