Designed for Data Scientists and Machine Learning Engineers

who want to use causal inference to help guide business decisions

You will:

Learn how to use a variety of causal inference tools common in the industry

Recognize when ignoring causality can lead to making poor decisions by learning from real-world examples

Understand the difference between causality and predictions

You should:

Be proficient with either Python or R

Be comfortable with college-level probability and statistics, such as expected values, confidence intervals, and linear regression

Possess basic familiarity with supervised machine learning

Learn live from a world-class


Learn live from a world-class


Rob Donnelly worked at Microsoft Research on statistical models of advertiser behavior that were used to predict how advertisers would respond to changes to the ad auction format. Then he studied economics at Stanford Graduate School of Business while doing research that combined machine learning and causal inference to better understand customer behavior. Susan Athey, one of the leading researchers in the area of machine learning and causal inference, was Rob’s advisor.

After earning his PhD, he worked on economic models of competition and network growth as a research scientist at Facebook and on advertising, pricing, and marketplace growth as a senior economist at Instacart. Now he is a machine learning scientist at a small startup called Arena-ai, which is focused on reinforcement learning for automating high-frequency business decisions.

Recommended by

World-Class Experts

Join a diverse and experienced


This cohort gives you access to a rich community of like-minded professionals 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 to recognize situations where causal inference can be applied and to evaluate the tradeoffs between different approaches to estimation.

About Rob’s

Live Cohort

If we want to use data to guide our decisions, we need to predict how our decisions will cause the world to change. Causal inference, the study of cause-and-effect, can be used to guide decision-making by predicting the outcomes our decisions will cause. A/B testing is a reliable and widely used approach for estimating causal effects and guiding decisions. However, for some important business decisions, we are unable to run randomized experiments for practical, ethical, or legal reasons.

This course will focus on causal inference approaches that can be done on observational data — meaning it does not come from a randomized experiment. You will learn to recognize situations where causal inference can be applied and evaluate the tradeoffs between different approaches to estimation.

Additionally, we’ll cover traditional statistical approaches along with recently developed techniques that use machine learning for causal inference. Along the way, you will also learn to recognize situations where careless use of machine learning models can confuse correlation with causation and generate bad decisions. Several case studies based on real problems faced by large tech companies will guide our conversations, and learners will practice implementing causal inference techniques on simulated data.

Session 1: An Introduction to Causal Inference
Tuesday, November 29
4-6 pm PST

Define causal inference and distinguish it from machine learning predictions

Recognize the key challenges to doing causal inference with observational databy exploring a case study on the impact of Amazon Prime on customer spending

Implement simple approaches for causal inference with linear regression

Session 2: Improving Our Causal Estimates by Comparing “Similar” Users
Thursday, December 1
4-6 pm PST

Use matching-based estimators to compare similar users who received different “treatments” (e.g., one Amazon Prime member compared to a non-member with similar characteristics)

Estimate propensity scores and use them for causal inference

Distinguish between situations where machine learning models may be able to generate reliable causal estimates and when they may not

Session 3: Taking Advantage of Quasi-Experiments
Tuesday, December 6
4-6 pm PST

Use event studies and difference-in-differences to estimate the long run impact of a late delivery

Combine propensity scores and outcome models to make “doubly robust” estimates

Reuse old A/B tests to make new causal estimates by using instrumental variables

Session 4: How Effective were my Facebook Ads? A Sobering Tale of the Limitations of Causal Inference
Thursday, December 8
4-6 pm PST

Explain the challenges of accurately measuring the true causal impact of advertising spending

Use double/debiased machine learning to estimate causal effects and drive incremental sales

Understand the limitations of causal inference approaches by evaluating a situation where causal inference approaches failed (even with Facebook-scale data)

Session 5: Heterogeneous Treatment Effects: Going Beyond Averages
Tuesday, December 13
4-6 pm PST

Contrast approaches for estimating user-specific causal effects  with estimating the population average effect by examining a case study about targeting advertising to drive incremental sales

Apply ML-based techniques for HTE: s-, t- and x- learner

Distinguish causal effect estimation from causal decision making

Session 6: More Approaches for Learning Heterogeneous Treatment Effects
Thursday, December 15
4-6 pm PST

Apply double/debiased machine learning for heterogeneous treatment effect estimation

Develop extensions of causal inference approaches for continuous variables such as price

Examine the tradeoffs between exploration and optimization

Still have questions?

We’re here to help!

Do I have to attend all of the sessions live in real-time?

You don’t! We record every live session in the cohort and make each recording and the session slides available on our portal for you to access anytime.

Will I receive a certificate upon completion?

Each learner receives a certificate of completion, which is sent to you upon completion of the cohort (along with access to our Alumni portal!). Additionally, Sphere is listed as a school on LinkedIn so you can display your certificate in the Education section of your profile.

Is there homework?

Throughout the cohort, there may be take-home questions that pertain to subsequent sessions. These are optional, but allow you to engage more with the instructor and other cohort members!

Can I get the course fee reimbursed by my company?

While we cannot guarantee that your company will cover the cost of the cohort, we are accredited by the Continuing Professional Development (CPD) Standards Office, meaning many of our learners are able to expense the course via their company or team’s L&D budget. We even provide an email template you can use to request approval.

I have more questions, how can I get in touch?

Please reach out to us via our Contact Form with any questions. We’re here to help!

Book a time to talk with the Sphere team