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
Causal Diagrams - Causal Inference
Today I talk about causal diagrams, e.g., dag, inline. This is one of the most important tools in Causal Inference, and we learn how to draw these tools out. Later we will learn how to use them in analysis.
From playlist Causal Inference - The Science of Cause and Effect
Double Blind - Causal Inference
In this video, I talk about the double blind assumption (both placebo effects and scientist preference effects) which serves as a good segue to causal diagrams, which I also go over. Enjoy!
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
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
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
We introduce Instrumental Variables
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
Violations of Exchangeability - Causal Inference
Today I talk about violations of exchangeability, e.g., common causes, confounding, selection bias.
From playlist Causal Inference - The Science of Cause and Effect
In this presentation from the Wolfram Technology Conference, Gerald Thomas explores some applications of decision process theory, which uses differential geometry techniques to predict future decisions. For more information about Mathematica, please visit: http://www.wolfram.com/mathemat
From playlist Wolfram Technology Conference 2012
Statistical Rethinking 2022 Lecture 20 - Horoscopes
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music: https://www.youtube.com/watch?v=g2GbpXqL5P8 Pause: https://www.youtube.com/watch?v=pxPdsqrQByM Chapters: 00:00 Introduction 08:26 Subjective responsibilities 14:36 Planning 34:34 Working 58:28 Re
From playlist Statistical Rethinking 2022
Meaning in Life & the Illusion of Free Will (Derk Pereboom)
Are human actions freely chosen? Can we deserve blame and praise for what we do? The common sense answer to both of these questions is yes. But this answer is threatened by the fact that our best scientific theories support the view that factors beyond our control produce all of our action
From playlist Free Will, Determinism, & Action
A conversation between Judea Pearl and Stephen Wolfram
A special session during the 2022 ISAIM conference, and in honor of Judea Pearl's 85th Birthday. Stephen Wolfram plays the role of Salonnière in this new, on-going series of intellectual explorations with special guests. Watch all of the conversations here: https://wolfr.am/youtube-sw-con
From playlist Conversations with Special Guests
Statistical Rethinking 2023 - 20 - Horoscopes
Course: https://github.com/rmcelreath/stat_rethinking_2023 Music: https://www.youtube.com/watch?v=g2GbpXqL5P8&t=0s Outline 00:00 Introduction 11:40 Planning 30:49 Working 54:41 Pause 55:15 Reporting 1:24:45 Science reform
From playlist Statistical Rethinking 2023
Bayesian Decision Flow Diagrams: An Agent Based Modeling....(Remote Talk) by Parantapa Bhattacharya
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)
TMCF workshop - Theory and methods challenges in counterfactual prediction, Karla Diaz-Ordaz
Prediction algorithms in AI use machine learning and statistics to make predictions about an event, given what we know now. Examples include whether a covid-19 patient will require ventilation, or whether a person seeking insurance will make a claim. These predictions can be used for plann
From playlist Theory and Methods Challenge Fortnights
System Dynamics: Systems Thinking and Modeling for a Complex World
MIT RES.15-004 System Dynamics: Systems Thinking and Modeling for a Complex World, IAP 2020 Instructor: James Paine View the complete course: https://ocw.mit.edu/RES-15-004IAP20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63Dur3imUjY08z92ypMphQ3 This one-day worksho
From playlist MIT OCW: RES.15-004 System Dynamics: Systems Thinking and Modeling for a Complex World, IAP 2020
Probability theory and AI | The Royal Society
Join Professor Zoubin Ghahramani to explore the foundations of probabilistic AI and how it relates to deep learning. đź””Subscribe to our channel for exciting science videos and live events, many hosted by Brian Cox, our Professor for Public Engagement: https://bit.ly/3fQIFXB #Probability #A
From playlist Latest talks and lectures
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
The Nature of Causation: The Counterfactual Theory of Causation
In this second lecture in this series on the nature of causation, Marianne Talbot discusses the counterfactual theory of causation. We have causal theories of reference, perception, knowledge, content and numerous other things. If it were to turn out that causation doesn’t exist, we would
From playlist The Nature of Causation