Causal Effects via Propensity Scores | Introduction & Python Code скачать в хорошем качестве

Causal Effects via Propensity Scores | Introduction & Python Code

💡 Get 30 (free) AI project ideas: https://30aiprojects.com/ This is the 2nd video in a series on causal effects. Here I introduce the Propensity Score and discuss 3 ways we can use it to compute causal effects from observational data. At the end, I share a concrete example with code of what using these methods might look like in practice. 👉 Series Playlist:    • Causality   📰 Read more: https://medium.com/towards-data-scien... 💻 Example Code: https://github.com/ShawhinT/YouTube-B... Resources: An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter C. Austin Data from UCI MLR: https://archive.ics.uci.edu/ml/datase... -- Homepage: https://shawhintalebi.com/ Introduction - 0:00 Observational vs Interventional Studies - 0:32 Propensity Score - 3:25 3 Propensity Score-based Methods - 4:56 1) Matching - 5:18 2) Stratification - 9:07 3) Inverse Probability of Treatment Weighting - 10:37 Example: ATE of Grad on Income - 12:29 Word of Caution - 15:46