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
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
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
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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
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
The Blessings of Multiple Causes - David M. Blei
Seminar on Theoretical Machine Learning Topic: The Blessings of Multiple Causes Speaker: David M. Blei Affiliation: Columbia University Date: January 21, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Causality: From Aristotle to Zebrafish - Frederick Eberhardt - 10/16/2019
Earnest C. Watson Lecture by Professor Frederick Eberhardt, "Causality: From Aristotle to Zebrafish." What causes what? If correlation does not equal causation, then how can we untangle the “why” behind processes that regulate the brain, the climate, or the economy? And how does this appl
From playlist Caltech Watson Lecture Series
SDS 613: Causal Machine Learning — with Emre Kiciman
#CausalMachineLearning #CausalInference #DoWhyOpenSource Dr. Emre Kiciman, Senior Principal Researcher at Microsoft Research joins the podcast to share his world-leading knowledge on causal machine learning. This episode is brought to you by Datalore, https://datalore.online/SDS, the col
From playlist Super Data Science Podcast
SDS 607: Inferring Causality — with Jennifer Hill
#DataScience #CausalInference #BayesianStatistics We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. This episode is brought to you by Pachyderm
From playlist Super Data Science Podcast
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
Post-Talk Discussion Following “On Experiments for Causal Inference and System Identification”
Professor Kyle Cranmer, Professor of Physics and Data Science at New York University, will engage Professor Nihat Ay in a discussion of his presentation “On Experiments for Causal Inference and System Identification.” In the first part of his presentation, Professor Nihat Ay of the Max P
From playlist Franke Program in Science and the Humanities
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
Tutorial on deep learning for causal inference
Speakers: Bernard Koch (SICSS-Los Angeles 19, 20, 21; Ph.D. student in Sociology at UCLA) Description: This tutorial will teach participants how to build simple deep learning models for causal inference. Although this literature is still quite young, neural networks have the potential to
From playlist All Videos
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