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
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
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
Examples of Selection Bias - Causal Inference
Today I talk about several distinct examples of selection bias.
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
Decision Trees are more powerful than you think
Let's talk about how decision trees can be used for modeling and causal inference! Follow me on M E D I U M: https://towardsdatascience.com/likelihood-probability-and-the-math-you-should-know-9bf66db5241b Joins us on D I S C O R D: https://discord.gg/3C6fKZ3E5m Please like and S U B S C R
From playlist Causal Inference
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
Assembly Bias as a Challenge to Infering the Galaxy-Dark Matter Connection - Andrew Zentner
Andrew Zentner - September 25, 2015 http://sns.ias.edu/~baldauf/Bias/index.html The interpretation of low-redshift galaxy surveys is more complicated than the interpretation of CMB temperature anisotropies. First, the matter distribution evolves nonlinearly at low redshift, limiting the
From playlist Unbiased Cosmology from Biased Tracers
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
Hiroshi Akashi - Codon usage bias in Drosophila: Population genetics and comparative genomics of
PROGRAM: School and Discussion Meeting on Population Genetics and Evolution PROGRAM LINK: http://www.icts.res.in/program/PGE2014 DATES: Saturday 15 Feb, 2014 - Monday 24 Feb, 2014 VENUE: Physics Auditorium, IISc, Bangalore Just as evolution is central to our understanding of biology, p
From playlist School and Discussion Meeting on Population Genetics and Evolution
Inference: A Logical-Philosophical Perspective - Moderated Conversation w/ A.C. Paseau and Gila Sher
Inference: A Logical-Philosophical Perspective. Moderated Conversation with Gila Sher, Department of Philosophy, University of California, San Diego on the talk by Alexander Paseau, Faculty of Philosophy, University of Oxford. The Franke Program in Science and the Humanities Understandi
From playlist Franke Program in Science and the Humanities
Genevera Allen, Rice University - Stanford Big Data 2015
Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.
From playlist Big Data in Biomedicine Conference 2015
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
Pearson's Correlation, Clearly Explained!!!
Correlation is one of the most basic statistical measures of how two different things might be related, which means it is very important to have a clear understanding of what it means and how it works. This StatQuest walks you through everything you need to know about Correlation. It tells
From playlist StatQuest
Statistical Rethinking Winter 2019 Lecture 05
Lecture 05 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. This lectures covers the material in Chapter 5 of the book, including multiple regression, intro to causal inference, and categorical variables.
From playlist Statistical Rethinking Winter 2019
Cosmological Tensions and the Early Universe by Sylvia Galli
PROGRAM: PHYSICS OF THE EARLY UNIVERSE - AN ONLINE PRECURSOR ORGANIZERS: Robert Brandenberger (McGill University, Montreal, Canada), Jerome Martin (Institut d'Astrophysique de Paris, France), Subodh Patil (Instituut-Lorentz for Theoretical Physics, Leiden, Netherlands) and L Sriramkumar (
From playlist Physics of The Early Universe - An Online Precursor
How to Set up the Null and Alternative Hypothesis
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How to Set up the Null and Alternative Hypothesis
From playlist 8.1 Basics of Hypothesis Testing