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
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 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
We introduce Instrumental Variables
From playlist Causal Inference - The Science of Cause and Effect
Intro to Epidemiology: Crash Course Public Health #6
Epidemiology is the study of patterns of diseases. And most people might think that means epidemiologists are only studying things like Ebola. But the truth is much more varied. In this episode of Crash Course Public Health, we'll take a look at the different ways Epidemiology is conducted
From playlist Public Health
D-Separation - Causal Inference
Today I talk about association in causal diagrams, e.g., D-separation. By applying the rules I outline in this video you will be able to determine if two variables are associated.
From playlist Causal Inference - The Science of Cause and Effect
Measurement Error - Causal Inference
In this video, I introduce our next assumption: measurement error, and make use of causal diagrams to further explain the assumption. Enjoy!
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
17. Reinforcement Learning, Part 2
MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: David Sontag, Barbra Dickerman View the complete course: https://ocw.mit.edu/6-S897S19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j In the first half, Prof. Sontag discusses h
From playlist MIT 6.S897 Machine Learning for Healthcare, Spring 2019
Susan Athey: Shaping the design of the data-driven marketplace
“Doing data science for business, especially the economic marketplaces at the heart of the new economy, is super interesting and super hard,” said Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business. “Off-the-shelf prediction and experimentation techn
From playlist Women In Data Science Conference (WiDS)- 2015
Driven Marketplace Design: Experiments, Machine Learning, and Econometrics | Susan Athey | WiDS 2015
Susan Athey, Stanford Graduate School of Business
From playlist Women in Data Science (WiDS)
SDS 539: Interpretable Machine Learning — with Serg Masís
#InterpretableML #MachineLearning #DataScience In this episode, Serg Masís joins the podcast to share his in-depth technical knowledge of Interpretable Machine Learning. Together they discuss why this field matters, how it’s evolving, and so much more. This episode is brought to you by U
From playlist Super Data Science Podcast
Conditional Average Treatment Effects: Overview
Professor Susan Athey presents an introduction to heterogeneous treatment effects and causal trees.
From playlist Machine Learning & Causal Inference: A Short Course
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
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
Confounding Example 2 - Causal Inference
Today I cover an example of an endogenous condition, a conditioned upon confounder (and collider) which is caused by the endogenous condition, and selection bias.
From playlist Causal Inference - The Science of Cause and Effect
Interpretability for Everyone - Been Kim
More videos on http://video.ias.edu
From playlist Mathematics
Loss Functions for Causal Inference
Professor Stefan Wager distills best practices for causal inference into loss functions.
From playlist Machine Learning & Causal Inference: A Short Course
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
I talk about the most common tools in Causal Inf that I won't be covering: * IP Weighting * Outcome Regresssion * Propensity Scores * G-estimation
From playlist Causal Inference - The Science of Cause and Effect