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
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
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
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
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
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
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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
Group Ideal Experiment - Causal Inference
In this video, I explain the concept of a group ideal experiment wherein I introduce some more causal inference terminology! I also go over the fundamental problem of causal inference and the problem of statistical inference. Enjoy!
From playlist Causal Inference - The Science of Cause and Effect
Mod-03 Lec-07 The Samkhya Philosophy - III
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
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer/Fall 2016 PSY 523 Psychology and Language lectures covering material from Harley's The Psychology of Language: From Data to Theory. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofd
From playlist PSY 523 Psychology and Language
HEDS | Stellar-Relevant Emission-Based Opacity Experiments at the Orion Laser Facility
HEDS Seminar Series- Madison Martin – October 21st, 2021 LLNL-VIDEO-838583
From playlist High Energy Density Science Seminar Series
Mathew Cherukara - HPC+AI-Enabled Real-Time Coherent X-ray Diffraction Imaging - IPAM at UCLA
Recorded 14 October 2022. Mathew Cherukara of Argonne National Laboratory presents "HPC+AI-Enabled Real-Time Coherent X-ray Diffraction Imaging" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: he capabilities provided by next generation light sources such as the Adva
From playlist 2022 Diffractive Imaging with Phase Retrieval - - Computational Microscopy
Mod-02 Lec-03 Carvaka Philosophy - I
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
September 29, 2008 lecture by Eugenie Scott for the Stanford Continuing Studies course on Darwin's Legacy (DAR 200). Dr. Scott explores the evolution vs. creationism debate and provides an argument for evolution. The lecture is concluded with a panel discussion with Brent Sockness and Je
From playlist Lecture Collection | Darwin's Legacy
After Math: Reasoning, Proving, and Computing in the Postwar United States - Stephanie Dick
More videos on http://video.ias.edu
From playlist Historical Studies
7. Machine Learning Tasks and Types
Machine learning is typically broken up into 4 types: supervised, unsupervised, semi-supervised, and reinforcement learning. But is this all? In this video, start by defining artificial intelligence, machine learning, and deep learning. We then cover the 14 tasks and types of machine learn
From playlist Materials Informatics
BioSci 94: Organisms to Ecosystems. Lec. 7. Origins of Life, Bacteria & Archaea
UCI BioSci 94: Organisms to Ecosystems (Winter 2013) Lec 07. Organisms to Ecosystems -- Origins of Life, Bacteria & Archaea -- View the complete course: http://ocw.uci.edu/courses/biosci_94_organisms_to_ecosystems.html Instructor: Michael Clegg, Ph.D. License: Creative Commons BY-NC-SA Te
From playlist BioSci 94: Organisms to Ecosystems
Inference in a Nonconceptual World, Brian Cantwell Smith and Joseph T. Rouse
Brian Cantwell Smith, Reid Hoffman Professor of Artificial Intelligence and the Human, University of Toronto. Moderated conversation with Joseph T. Rouse, Department of Philosophy, Wesleyan University. Classical models of inference, such as those based on logic, take inference to be *conce
From playlist Franke Program in Science and the Humanities