From playlist Exploratory Data Analysis
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
Observational Studies - Causal Inference
Today I talk about how observational studies are great examples of when causation does not equal association by visiting a real world example. The next videos will explore how we extract causal information from observational studies
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
Stanford Seminar - Theories of inference for visual analysis
Jessica Hullman Northwestern University December 3, 2021 Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design p
From playlist Stanford Seminars
Transparency and Reproducibility in Observational Research: Lessons From Anthropology
Melanie Martin and Bret Beheim discuss reproducibility in observational research and examine particular problems as demonstrated by anthropological research.
From playlist Yale Day of Data 2016
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
We talk about an added assumption of the parametric G formula
From playlist Causal Inference - The Science of Cause and Effect
We introduce Instrumental Variables
From playlist Causal Inference - The Science of Cause and Effect
How to pick a machine learning model 5: Navigating assumptions
Part of the End-to-End Machine Learning School course library at http://e2eml.school Use this in an End to End Machine Learning project: https://end-to-end-machine-learning.teachable.com/p/polynomial-regression-optimization/ Blog post: https://brohrer.github.io/how_modeling_works_5.html
From playlist E2EML 171. How to Choose Model
Lecturer: Dr. Erin M. Buchanan Fall 2020 https://www.patreon.com/statisticsofdoom This video is part of my structural equation modeling class - you will learn about SEM terminology, degrees of freedom, specification, and start to see some lavaan output. You can learn more at: https://
From playlist Structural Equation Modeling 2020
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
SICSS 2017 - Moving Beyond Simple Experiments (Day 6. June 24, 2017)
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 SICSS 2017 - Experiments (6/24)
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
[34] Taking the Edge Off of Data Science with dabl (Andreas Mueller)
## Upcoming Events Join our Meetup group for more events! https://www.meetup.com/data-umbrella [34] Andreas Mueller: Taking the Edge Off of Data Science with dabl Exploratory Data Analysis ## Key Links - Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2021/34-an
From playlist talks
When to use which connectivity method?
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #4) Synchronization
Dictionary-Based Text Analysis
In this video, Professor Chris Bail of Duke University introduces dictionary-based text analysis methods, and discusses the tradeoffs of different lexicons for studying human behavior and computational social science. Link to slides: https://compsocialscience.github.io/summer-institute/202
From playlist SICSS 2020
Principles of Graphics HD 720p
From playlist Exploratory Data Analysis
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
From playlist All Videos