Designed for technology professionals looking to go from 0 to 100 in

Causal Inference

Data Scientists

looking to understand the important distinction between prediction and causal inference

Machine Learning Engineers

who want an introduction to the core material that supports more contemporary machine learning on causal inference

Introduction to design and model based causal inference for when randomized A/B tests are not feasible
Instruction on implementing most commonly conducted research designs based on non-experimental data (including regression discontinuity, instrumental variables, synthetic control and difference-in-differences)
Practical guidance on implementing these estimation strategies in python and R

Product Managers

who want guidance on interpreting causal analysis and directions for conducting it

Clear exposition on how causal inference is distinct from predictive analytics
Examples of when causal questions can and should be pursued to help guide product strategy
Understand when and how to implement the research designs and strategies for estimating causal effects when A/B tests are not possible

Meet your

Instructor

Prof. Scott Cunningham

Author of Causal Inference:
The Mixtape

Professor at Baylor University

Scott Cunningham is a Professor of Economics at Baylor University.   He is also the author of the recent book on causal inference, Causal Inference: The Mixtape. He is in high demand as a teacher of causal inference and has taught several dozen workshops on the subject with large scale enterprises (including Meta, Twitch, Etsy), governments and universities  all over the world.

He has published in top journals of economics and is passionate about teaching causal inference to as many as he can because he believes strongly it can help people better understand the impact of our private and public choices on society.
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He did his PhD in economics from the University of Georgia in 2007 and has been at Baylor ever since.  His research is in health and labor economics like mental health care, corrections, suicide, sex work, violence against women, abortion policy and drug policy.  

Meet your

Instructor

Prof. Scott Cunningham

Prof. Scott Cunningham

Author of Causal Inference: The Mixtape

Professor at Baylor University

Scott Cunningham is a Professor of Economics at Baylor University.   He is also the author of the recent book on causal inference, Causal Inference: The Mixtape. He is in high demand as a teacher of causal inference and has taught several dozen workshops on the subject with large scale enterprises (including Meta, Twitch, Etsy), governments and universities  all over the world.

He has published in top journals of economics and is passionate about teaching causal inference to as many as he can because he believes strongly it can help people better understand the impact of our private and public choices on society.
‍
He did his PhD in economics from the University of Georgia in 2007 and has been at Baylor ever since.  His research is in health and labor economics like mental health care, corrections, suicide, sex work, violence against women, abortion policy and drug policy.  

Accompanied by Guest Instructors with experience from

Leading Companies

Brian Quistorff

Ex-Senior Researcher @ Microsoft

Discussing his work on synthetic control at Microsoft

Pedro Sant'Anna

Senior Researcher @ Microsoft

Exploring difference-in-difference in practice at Microsoft

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Be part of the future of online learning

What you get out of

This Course

Learn live from an industry expert

Scott has worked with leading companies, governments and universities around the world to enhance their design-based causal inference skills. He’s written a book and delivered multiple courses on the topic and now wants to share his knowledge with professionals around the world

Join a lifelong professional network of executives

This course gives you access to a rich community of like-minded executives from some of the best businesses in the world. Even after the course ends, you will continue to learn and build with each other.

Exclusive content to advance your business

Get access to exclusive content through live sessions, meetups and our Student Portal (even after you finish the cohort). Learn and understand the gold standard in science that has transformed the way software is being developed

About Scott’s

Live Cohort

For most of human history, scientists worked with small amounts of data by today’s standards.  But that is no longer the case – today we are drowning in data, and everyone is expected to know how to use it.  This course is important because it lays out the difference between prediction and causal inference, and focuses on the latter.  This will help decision makers better understand when the tool calls for a causal question to be answered and when it calls for a predictive answer.  Failure to make such distinctions can lead to tragic mistakes in judgment.
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The modern field of causal inference is a blend of statistics and econometrics built on a theory of counterfactuals and the potential outcomes framework.  This course is primarily based on the design tradition whose architects were awarded the 2021 Nobel Prize in economics (David Card, Josh Angrist and Guido Imbens), as opposed to the model-based tradition of causal inference found in Judea Pearl’s work, though they are complements not substitutes.  
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This course trains you in the art and science of causal inference by studying closely the main research designs within this field – regression discontinuity, instrumental variables, difference-in-differences and synthetic control – as well as the estimators that are often best suited for them.  It will also advance your knowledge through spending time going over applications in R and python.
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The goals and learning objectives of this course are to develop a solid understanding of:
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Design based causal inference, including the major research designs of regression discontinuity, difference-in-differences, instrumental variables and synthetic control

Practically implementing these designs in R and python, including interpreting output

Session 1 - Potential outcomes

June 6
4 - 6 pm PT

We will learn the potential outcomes model which provides the mathematical and statistical notation for all design-based causal inference. Specifically, we will:

Use this notation to lay out key aggregate causal parameters like the average treatment effect,

Illustrate the source of selection bias using a decomposition of simple comparisons and note the problems created when people choose treatments based on expected personal gains

Show why randomization is such an important feature in causal inference

Sessions 2 & 3 - Diff-in-Diff

June 7 & 13
4 - 6 pm PT

The most popular quasi-experimental method in design-based causal inference is the difference in differences. The intuition and setup are simple: a group of treated units are observed after treatment and before treatment which are then compared to another group of untreated units after and before. In these three sessions, we will:

Learn models developed to correctly obtain unbiased estimates of the average treatment effect on the treated units under a variety of common situations (e.g., differential timing, controlling for covariates)

Develop a deeper understanding of how one might obtain some indirect evidence that the method’s key assumptions are reasonable to make

Implement these methods using code and data

Guest Appearence: Pedro Sant’Anna (Microsoft)

Sessions 4 & 5 - Synthetic Control

June 15 & 20
4 - 6 pm PT

Susan Athey, former Chief Economist atMicrosoft and John Bates Clark award winner, and Guido Imbens, the 2001 co-recipient of the Nobel Prize, have called synthetic control one of the most important innovations in causal inference of the last twenty years. In this session, we will:

Learn the traditional method developed by Alberto Abadie and coauthors,

Practice its implementation using code and sample data

Provide some guidance about ways to correctly augment the method when certain challenges present themselves

Guest Appearence: Brian Quistorff (Bureau of Economic Analysis, former Microsoft )

Sessions 6 - Regression Discontinuity

June 22
4 - 6 pm PT

Inferring causality always involves understanding how people or things are moved into and out of some intervention. In the A/B test, that involves randomization. With regression discontinuity designs, it involves a score. When people or things possess a score over some cutoff, they are moved into an intervention and regression discontinuity formalizes this process so that we can obtain valid estimates of the causal effect of the intervention on outcomes we care about. In this session, we will:

Learn the estimation techniques that are essential to obtaining causal effects when treatment assignment is based on scores as opposed to randomization

Practice RDD using code and large administrative data from a government agency

Provide suggestions about data visualization

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