Causal Modeling in Machine Learning Workshop

The most efficient path to engineering causal reasoning in machine learning applications.

A online workshop in causal modeling and causal inference in a machine learning context.

  • At-your-own-pace online learning with one-on-one meetings with instructors
  • Short digestible course modules and lectures
  • Enough depth to get full level mastery of the field.


Workshop Details

Length: 12 weeks
Current Cohort (in progress): 8-30-2021 to 11-15-2021
Next Cohort: October (tentative)
Cost: $1199 + tax. 30% reduction for students enrolled in universities.

  • One-on-one scheduling and weekly retros
  • Continuous office hours via chat, instructor feedback on coding assignments
  • Optional portfolio project in causal ML that you can showcase publicly. Get instructor guidance and iterative feedback on portfolio project.
  • Gain ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams at top-tier technology organizations.
  • Master modern programming libraries, including a deep learning framework, in the implementation of causal reasoning algorithms.

"I thoroughly enjoyed the course. I made friends along the way such as Jeremy, Harish and Nalin. Altdeep really shines on instructor office hours that allows students to ask questions. Students can DM the instructors directly. This is a differentiating factor for Altdeep. I interacted with other students and TAs on the forum a lot, which helped with learning. I also enjoyed the homework assignments and class project. Having a capstone project that student can put on their Github as portfolio is valuable. I have my name on Robert's Github for causal OOP, which is awesome! I also get to learn about other students’ projects. Double awesomeness!"

- Shih-Gian Lee, Senior Machine Learning Engineer

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What makes this course different?

Studying Causality

  • High-level approach unites causal inference ideas across multiple fields, including econometrics, Bayesian modeling, potential outcome models, and structural causal models.
  • Focuses on generative machine learning and problems typical of industrial data science, as opposed to applied statistical methods in social science.
  • Has heavy focus on Python code and libraries.
  • Connections to deep learning and probabilistic programming with PyTorch-based modeling language Pyro.

Course Curriculum


  Information and Announcements
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  Wrapping Up
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I am a senior software developer with backgrounds in information retrieval, data visualization, data science, and decision support applications. I am working on decision support tools which simulate responses to "what if" questions through systems models.

"What if" questions are causal questions. That's a focus outside what machine learning typically addresses. I had been doing a lot of "causal" reading, mostly Bayesian statistics papers and pymc3 notebooks, but I didn't have a good map of the terrain. I was most interested in what Charles Isbell calls "mindset", the organizing concepts and the thinking behind the modeling techniques.

As a comprehensive survey, Robert's class came along at just the right time for me. Robert Osazuwa Ness is a creative researcher working at the state of the art. I found his lectures inspiring. Robert is also gifted at explaining things clearly and concretely. The interactive seminars were great, I could get answers to my questions. The class delivered as advertised.

Thanks to this class, I now know how to code a set of problems that are important to me. The class projects are important models there. I know where to invest further in terms of libraries and tooling. In terms of causal modeling and estimation, I have a feeling for what matters, what's hard, what lines of research to follow, and I now know enough to read research papers in the field. "Mindset" is difficult to quantify, but I'll say this survey delivers a map of the terrain that one can navigate with independently.

- Eric Moore, Principal at Decision Rubric

Hear from a Top Tech Industry Computation Causal Inference Engineer

Hear from Jeffrey Wong, Principal Data Scientist: Computational Causal Inference at Netflix


Hear from a Past Student (Research Scientist)

Hear a Pacific Northwest National Labs research scientist describe their experiences with the workshop.


Frequently Asked Questions


How long is the cohort?
The cohorts last 12 weeks. During that time, you can schedule appointments with instructors during you enrollment period. However, the course material never ends! It is a completely self-paced online course - you decide when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any devices you own.
Will you refund me if I'm unhappy?
Yes! We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days, and we will give you a full refund.
What do you mean by "online workshop" and "cohort"? What kind of access will I have to course instructors?
Altdeep runs cohort-based online workshops. A workshop has an enrollment period. During the enrollment period you get access to instructors through the course forum. This includes group chats and one-on-ones (by appointment). Topics can range from coding assignment help to general questions and advice. You also get homework and project work checked by humans. You get guidance and frequent feedback on portfolio projects. You receive guidance and frequent feedback on portfolio projects. Finally, you have the ability to engage with a community of other students.
Where can I check out the course materials?
The course materials are available in the course repository at https://www.github.com/robertness/causalML.
What programming languages and libraries do we use in the workshop?
In this workshop, we rely primarily on a Pytorch-based probabilistic programming language called Pyro. We use some graphical modeling software in Python (pgmpy) and R (bnlearn).
How does the class project work?
If you like, instructors will work with you on a capstone project. You can see examples of previous projects by previous students here: https://bit.ly/causal_projects
What would you say would be a reasonable estimate for the (weekly) amount of time required to get a good grasp of the materials?
We designed this workshop so that you may remain at a high-level view if you prefer, and you may go deeper and get a deep grasp of the material if you prefer. For the high-level approach, you just watch videos, and read lecture notes, read through the forum, and ask questions in calls with the instructors. That requires about a half-hour a day for the four month enrollment period. A deeper study is where you do the homework and the capstone project. That level of commitment is the same as a graduate school level course, which is 6-10 hours a week. A deeper study is where you do the homework and the capstone project. That level of commitment is the same as graduate school level course, which is 6-10 hours a week.
What is the basic mathematical background I need to succeed?
You nee a basic understanding of random variables, joint probability, conditional probability, and expected value. You also want an understanding of Baye's rule and intuition about Bayesian reasoning. Finally, you should have an understanding of probability mass functions (plug in an outcome, get the probability of that outcome).
Will the lectures change?
Yes. We update the lectures with new videos and content based on developments in the field and feedback from students.
I do not have software engineering-level Python skills. Is this workshop right for me?
Yes. You just need to be comfortable with a language like Python, R, or Julia to do a data science-style analysis. The biggest coding challenge is usually debugging code when working on homework. For that, you can get help from instructors.
I am familiar with/interested in a particular causal inference method. I am interested in how it fits into the big picture. Will you cover this method?
Yes! Your interest is a signal that we should cover it, so we will if you ask.
How do I interact with the instructors and other students
Upon enrollment you will be able to access a course forum. The forum is a great place to discuss workshop materials with other students, instructors, as well as participate in a community of like-minded individuals.
How does this course compare with X course?
We strive to connect probabilistic modeling with causal modeling. This focus, and the focus on machine learning and AI, are the main differentiators in terms of content from other courses. Importantly, this workshop features weekly retrospective calls, grading of assignments by humans, direct access to instructors, and access to an active community forum.
I don't want to pay the fee. How do I learn causal modeling for free?
Checkout the workshop repository at www.github.com/robertness/causalml for free tutorials. If you are looking for good textbooks, we recommend Elements of Causal Inference: Foundations and Learning Algorithms by Peters, Janzing, and Scholkopf.

Hear from a Past Student (Industry)

Hear from former student Vince Kennedy, Senior Associate, Strategy & Analytics at Penn Foster


Your Instructor


Robert Osazuwa Ness
Robert Osazuwa Ness

Robert didn't start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.
After switching to the tech industry, Robert's interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups. He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.


Have a background in AI?

Hear from former students who are AI researchers in industry.


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