Data Science Decisions in Time: Using Causal Information
Master causal modeling techniques for data science decision-making, from A/B testing to personalized medicine. Perfect for intermediate analysts.
Master causal modeling techniques for data science decision-making, from A/B testing to personalized medicine. Perfect for intermediate analysts.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Decisions in Time Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Develop and implement causal decision frameworks
Master A/B testing through a causal lens
Apply causal random forests for decision optimization
Analyze multiple cause scenarios
Design individual treatment effect studies
Optimize business decisions using causal models
Skills you'll gain
This course includes:
2.5 Hours PreRecorded video
10 quizzes, 6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores advanced causal modeling techniques for data-driven decision making. Students learn to develop and analyze causal relationships in various contexts, from business decisions to healthcare applications. The curriculum covers sequential causal decisions, causal random forests, multiple causes analysis, and individual treatment effects. Through practical examples in supermarket pricing, restaurant location optimization, and personalized medicine, students gain hands-on experience in applying causal inference methods to real-world problems.
Sequential Causal Decisions
Module 1 · 4 Hours to complete
Is that a Causal Decision or a Causal Effect?
Module 2 · 4 Hours to complete
Causal Random Forests
Module 3 · 4 Hours to complete
Blessings of Multiple Causes
Module 4 · 4 Hours to complete
Individual Treatment Effects and Personalized Medicine
Module 5 · 4 Hours to complete
Untitled Module
Module 6 · 6 Hours to complete
Fee Structure
Instructor
Distinguished Biophysicist and Computational Science Leader at Johns Hopkins
Dr. Thomas Woolf serves as a Professor at Johns Hopkins University School of Medicine since 1994, bringing expertise in biophysics and computational science. After earning his Ph.D. in Biophysics from Yale University and B.S. in Physics from Stanford University, he has established himself as a leader in membrane protein research and computational biophysics. His work combines high-performance computing, machine learning, and molecular dynamics to understand complex biological systems. As director of his research lab, he focuses on studying membrane proteins using advanced computational methods and the molecular dynamics program CHARMM. Beyond his academic work, Dr. Woolf is also CEO and co-founder of DaiWare, Inc., a healthcare company focusing on patient data interpretation using streaming data and machine learning technologies. He teaches courses in stochastic differential equations, probabilistic graphical models, and statistics, while conducting research in time-series analysis, cellular biophysics, and metabolic processes.
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