Expert Review: Causal Inference
By Prosun • December 21, 20254.9/5.0
Our Expert Verdict
Verdict: Causal Inference is unequivocally the leading program in its category for 2026. Our expert review team scored it a **4.9/5.0** for its comprehensive curriculum and direct career impact.
Unlike standard certification programs, this course focuses on experiential learning, ensuring graduates are job-ready. If you are serious about mastering Columbia University, this is a definitive investment.
Enroll Now & Get Certified ↗What We Liked (Pros)
- Unmatched depth in Columbia University methodology.
- Capstone project perfect for portfolio building.
- Taught by industry leaders from Columbia University.
- Flexible learning schedule that fits professional life.
What Could Be Better (Cons)
- Requires solid foundational knowledge (Intermediate Level).
- Certification fee is higher than average.
Course Overview
This course, provided by Columbia University, is characterized by its rigor and practical application focus. The curriculum covers essential concepts: offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. provides an introduction to the statistical literature on causal inference that has emerged in the last 3540 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships. Students will how to distinguish between relationships that are causal and noncausal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, subclassification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to .
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