Curated learning path for Uplift Modeling & Causal ML. Build practical skills through expert-selected courses.
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Basic statistics helpful; will be taught
Some coding experience; Python or R preferred
Follow these courses in order to complete the learning path. Click on any course to enroll.
An online workshop on causal modeling and inference within a machine learning context, designed for self-paced learning.
An open and free course from Carnegie Mellon University that introduces causal and statistical reasoning for critical thinking.
A series of summer courses offered by Harvard's CAUSA Lab, providing in-depth training on causal inference.
This course focuses on the methods used to measure causal effects in the social sciences, a key area for practitioners.
An interactive Data Camp course teaching the fundamentals of causal inference and how to implement various methods in R.
A series of workshops based on the popular book Causal Inference: The Mixtape, covering foundational and advanced topics in a hands-on manner with code in R and Stata.
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See the side-by-side breakdown and our pick by scenario
See the side-by-side breakdown and our pick by scenario
Curated learning path for Uplift Modeling & Causal ML. Build practical skills through expert-selected courses.
You'll work through Understand core concepts and foundations; Build your first projects with guided tutorials; Gain confidence with hands-on exercises; Perform exploratory data analysis (EDA) on complex datasets; Clean and preprocess data for analysis; Create compelling data visualizations.
It's pitched at beginner level — a solid starting point if you're new to the topic.
6 curated courses, sequenced from foundational to advanced.
Around $85 total if you buy every course — but many include free audit options.
Enroll in this path to track your progress and stay motivated.