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
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
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
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
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
Observational Studies - Causal Inference
Today I talk about how observational studies are great examples of when causation does not equal association by visiting a real world example. The next videos will explore how we extract causal information from observational studies
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
Crossover Random Experiment - Causal Inference
In this video, I explain the concept of a crossover random experiment which is essentially the practical/normal version of a single individual idealized experiment (which we covered in the previous video: https://youtu.be/bJ0dlGkYga0 of the Causal Inference series).
From playlist Causal Inference - The Science of Cause and Effect
Univalent Foundations Seminar - Steve Awodey
Steve Awodey Carnegie Mellon University; Member, School of Mathematics November 19, 2012 For more videos, visit http://video.ias.edu
From playlist Mathematics
This is part of a series of lectures on the Zermelo-Fraenkel axioms for set theory. We discuss the axiom of infinity, and give some examples of models where it does not hold. For the other lectures in the course see https://www.youtube.com/playlist?list=PL8yHsr3EFj52EKVgPi-p50fRP2_SbG
From playlist Zermelo Fraenkel axioms
Attribution Theory (Examples and What it is)
Learn more about Attribution Theory on my blog! https://practicalpie.com/attribution-theory/ Enroll in my 30 Day Brain Bootcamp: https://practicalpie.com/30-day-brain-bootcamp-plan/ --- Invest in yourself and support this channel! --- ❤️ Psychology of Attraction: https://practicalpie.com
From playlist Social Psychology
Sloppiness and Parameter Identifiability, Information Geometry by Mark Transtrum
26 December 2016 to 07 January 2017 VENUE: Madhava Lecture Hall, ICTS Bangalore Information theory and computational complexity have emerged as central concepts in the study of biological and physical systems, in both the classical and quantum realm. The low-energy landscape of classical
From playlist US-India Advanced Studies Institute: Classical and Quantum Information
Univalent foundations and the equivalence principle - Benedikt Ahrens
Vladimir Voevodsky Memorial Conference Topic: Univalent foundations and the equivalence principle Speaker: Benedikt Ahrens Affiliation: University of Birmingham Date: September 12, 2018 For more video please visit http://video.ias.edu
From playlist Vladimir Voevodsky Memorial Conference
Gene Regulation in Space and Time by Caroline Uhler
Information processing in biological systems URL: https://www.icts.res.in/discussion_meeting/ipbs2016/ DATES: Monday 04 Jan, 2016 - Thursday 07 Jan, 2016 VENUE: ICTS campus, Bangalore From the level of networks of genes and proteins to the embryonic and neural levels, information at var
From playlist Information processing in biological systems
SLT and Alignment Pt 2 - Singular Learning Theory Seminar 39
Dan Murfet presents the second part of the two part series on connections between Singular Learning Theory and AI alignment. The topics discussed: - Structure of knowledge vs structure of singularities - How structure of neural networks accumulates over training - Critical weights and pha
From playlist Singular Learning Theory
Fellow Short Talks: Professor Zoubin Ghahramani, University of Cambridge
Bio Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group, and The Alan Turing Institute’s University Liaison Director for Cambridge. He is also the Deputy Academic Director of the Leverhulme Centre for the
From playlist Short Talks
Dr Natalia Bochkina, Edinburgh University
Natalia Bochkina joined the University of Edinburgh as a Lecturer in Statistics in 2007. In 2003-2007 she was a postdoc at the Biostatistics group at the Imperial College London working on the collaborative project building a biological atlas of insulin resistance. In 2002-2003 she was a s
From playlist Short Talks
ML Tutorial: Bayesian Machine Learning (Zoubin Ghahramani)
Machine Learning Tutorial at Imperial College London: Bayesian Machine Learning Zoubin Ghahramani (University of Cambridge) January 29, 2014
From playlist Machine Learning Tutorials
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