Causal Behavioral Modeling Framework - Discrete Choice Modeling of Consumer Demand
There are increasing demands for "causal ML models" of the agent behaviors, which enable us to unbox the complex black-box models and make inferences or do proper counterfactual simulations. Many applications (especially in Marketing) intrinsically call for measurement of the causal impact
From playlist Fundamentals of Machine Learning
Survivorship Bias - Examples, Definitions, and String Art - Cognitive Biases
The Survivor Bias, also know as the survival or survivorship bias, is a commonly committed cognitive bias in the field of business and science. When people make assumptions from data without understanding where all the data is coming from, they are falling victim to a great example of a su
From playlist Cognitive Biases
We introduce Instrumental Variables
From playlist Causal Inference - The Science of Cause and Effect
Elina Robeva: "Hidden Variables in Linear Non-Gaussian Causal Models"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop III: Mathematical Foundations and Algorithms for Tensor Computations "Hidden Variables in Linear Non-Gaussian Causal Models" Elina Robeva - University of British Columbia Abstract: Identifying causal
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
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
We talk about an added assumption of the parametric G formula
From playlist Causal Inference - The Science of Cause and Effect
Causal inference in observational studies: Emma McCoy, Imperial College London
Emma McCoy is the Vice-Dean (Education) for the Faculty of Natural Sciences and Professor of Statistics in the Mathematics Department at Imperial College London. Her current research interests are in developing time-series and causal inference methodology for robust estimation of treatment
From playlist Women in data science conference
Julie Josse: Treatment effect estimation with missing attributes
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 04, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Julie Josse: Treatment effect estimation with missing attributes
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 04, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Large Sample Size - Causal Inference
In this video, I introduce the assumption of having a large sample size and talk a little about how large sample size combats sampling variance and non deterministic unit level causal effects. Enjoy! Link to stat inference videos: https://www.youtube.com/watch?v=uWLMtCtsHmc&list=PLgJhDSE2
From playlist Causal Inference - The Science of Cause and Effect
Judea Pearl: "Interpretability and explainability from a causal lens"
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Interpretability and explainability from a causal lens" Judea Pearl - University of California, Los Angeles (UCLA), Computer Science Abstract: I will describe the task
From playlist Machine Learning for Physics and the Physics of Learning 2019
No Cause for Concern: Indefinite Causal Ordering as a Tool for Understanding Entanglement
Understanding the sorts of explanations and inferences that causal processes countenance is of course of great interest to philosophers and physicists (among others). But what can be said about physical processes that fail to exhibit classical causal structure? Indefinite causal ordering
From playlist Franke Program in Science and the Humanities
Statistical modeling and missing data - Rod Little
Virtual Workshop on Missing Data Challenges in Computation, Statistics and Applications Topic: Statistical modeling and missing data Speaker: Rod Little Date: September 8, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
We talk about three times when IV analysis was used in real studies
From playlist Causal Inference - The Science of Cause and Effect
MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: David Sontag View the complete course: https://ocw.mit.edu/6-S897S19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j Prof. Sontag discusses causal inference, examples of causal q
From playlist MIT 6.S897 Machine Learning for Healthcare, Spring 2019
AIMI Symposium 2020 - Session 5: Fairness in Clinical Machine Learning
Session 5 focuses on issues of equity, bias, and strategies to achieve fairness in clinical AI applications 00:00 - Session Overview Kristen Yeom - Associate Professor of Radiology and, by courtesy, of Neurosurgery; Stanford 00:41 - AI, Medicine, & Bias: Diversifying Your Dataset is Not
From playlist Rachel Thomas videos
Public Health Seminar. Structural Considerations in Social Epidemiologic Analysis.
Recorded October 11, 2013. Jay Kaufman, Ph.D.
From playlist Public Health: Graduate Seminars (2013 - 2015)
Nicolas Franco: Causal information from Lorentzian spectral triples
Lorentzian spectral triples are an attempt to adapt noncommutative geometry to Lorentzian signature using the notion of Krein space and fundamental symmetry. From the data given by the Dirac operator and the fundamental symmetry, a causal structure can be defined on the space of states. Fo
From playlist HIM Lectures: Trimester Program "Non-commutative Geometry and its Applications"
20170621 Lecture by Brandon Stewart
The first Summer Institute in Computational Social Science was held at Princeton University from June 18 to July 1, 2017, sponsored by the Russell Sage Foundation. For more details, please visit https://compsocialscience.github.io/summer-institute/2017/
From playlist All Videos