Causal Inference is a set of tools used to scientifically prove cause and effect, very commonly used in economics and medicine. This series will go over the basics that any data scientist should understand about causal inference - and point them to the tools they would need to perform it.
From playlist Causal Inference - The Science of Cause and Effect
Brief Introduction to Statistical Inference - Causal Inference
In this video, I briefly introduce the topic of Statistical Inference and go over its most fundamental concepts - those that we will use in this series. If you want to learn more about this stuff, check out this link to my entire series on Statistical Inference: https://www.youtube.com/pla
From playlist Causal Inference - The Science of Cause and Effect
Fundamental Question - Causal Inference
In this video, I define the fundamental question and problem of causal inference and use an example to further explain the concept.
From playlist Causal Inference - The Science of Cause and Effect
Ideal Experiment - Causal Inference
In this video, I give you more details about the fundamental question and the fundamental problem of causal inference with the help of an example (our ideal experiment).
From playlist Causal Inference - The Science of Cause and Effect
Statistical Inference for Causal Inference - Causal Inference
In this video I explain the concept of statistical inference for causal inference through a realistic group ideal experiment example. Enjoy! Here's the link to my previous Statistical Inference Introduction video if you haven't watched it yet: https://youtu.be/fEGc8ZqveXM
From playlist Causal Inference - The Science of Cause and Effect
Assumptions - Causal Inference
In this video, I introduce the most important assumptions in casual inference that we use in order to avoid mistakes such as presuming association and causation to be one and the same, among others: - Positivity - SUTVA - Large Sample Size - Double Blinded - No Measurement Error - Exchan
From playlist Causal Inference - The Science of Cause and Effect
We describe my favorite causal inference technique: the parametric G formula, my go-to for any standard observational causal inference problems
From playlist Causal Inference - The Science of Cause and Effect
Thanks so much for watching! Please comment below on what topics you'd like to see covered next!
From playlist Causal Inference - The Science of Cause and Effect
Causation vs. Association - Causal Inference
In this video I talk about the difference between causation and association and explain each of these concepts through an example. Enjoy!
From playlist Causal Inference - The Science of Cause and Effect
Mod-05 Lec-21 The Nyaya Philosophy - VII
Indian Philosophy by Dr. Satya Sundar Sethy, Department of Humanities and Social Sciences, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist IIT Madras: Introduction to Indian Philosophy | CosmoLearning.org Philosophy
Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Mod-02 Lec-04 Carvaka Philosophy - II
Indian Philosophy by Dr. Satya Sundar Sethy, Department of Humanities and Social Sciences, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist IIT Madras: Introduction to Indian Philosophy | CosmoLearning.org Philosophy
Oxford 4b The Argument Concerning Induction
A course by Peter Millican from Oxford University. Course Description: Dr Peter Millican gives a series of lectures looking at Scottish 18th Century Philosopher David Hume and the first book of his Treatise of Human Nature. Taken from: https://podcasts.ox.ac.uk/series/introduction-david
From playlist Oxford: Introduction to David Hume's Treatise of Human Nature Book One | CosmoLearning Philosophy
Mod-05 Lec-20 The Nyaya Philosophy - VI
Indian Philosophy by Dr. Satya Sundar Sethy, Department of Humanities and Social Sciences, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist IIT Madras: Introduction to Indian Philosophy | CosmoLearning.org Philosophy
This video functions as a brief introduction to many different topics in formal logic. Notes on the Images: I looked into the legality of using images for this video a good deal and I've come to the conclusion that there is nothing in this video which could remotely imply these images ar
From playlist Summer of Math Exposition 2 videos
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources
10. Origin, Destination, and Transfer Inference
MIT 1.258J Public Transportation Systems, Spring 2017 Instructor: Nigel Wilson, Gabriel Sanchez-Martinez, Neema Nassir View the complete course: https://ocw.mit.edu/1-258JS17 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62AU7iNniqVoMl8C64tIOVk This lecture discussed
From playlist MIT 1.258J Public Transportation Systems, Spring 2017
Exchangability: Part 1 - Causal Inference
In this video, I introduce and explain our most important and perhaps hardest to grasp causal inference assumption so far: exchangability. Enjoy!
From playlist Causal Inference - The Science of Cause and Effect
Stephen Green - Real-time gravitational-wave parameter estimation using machine learning
Recorded 17 November 2021. Stephen Green of the Max Planck Institute for Gravitational Physics, Albert Einstein Institute presents "Real-time gravitational-wave parameter estimation using machine learning" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational W
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy