Oreilly – Causal Inference for Data Science 2025-1

Causal Inference for Data Science. This course teaches participants how to combine statistical methods and machine learning to identify causal relationships between events and measure their effects in order to improve outcomes. In the world of data science, understanding the cause of events is key because it allows for intelligent intervention rather than mere prediction. When conducting controlled experiments such as A/B testing is difficult or expensive, this course teaches alternative techniques to discover causal relationships using existing data. Topics include modeling reality with causal graphs, estimating effects with statistical methods, choosing the most appropriate tool (A/B testing, machine learning, or causal inference), and evaluating the assumptions and limitations of the analysis. You will also learn how to analyze historical data, understand customer behavior, and facilitate effective decision-making by identifying key drivers. This course will provide you with the tools necessary to build models that not only predict patterns but also reveal the underlying causes.

What you will learn

• Modeling reality using causal graphs.
• Estimating causal effects using statistical and machine learning techniques.
• Determining the right time to use A/B testing, causal inference, and machine learning.
• Explain and evaluate objectives, assumptions, risks, and limitations.
• Determine whether you have enough variables for your analysis.
• How to evaluate advertising performance, select effective treatment methods, provide effective product pricing, and more.

This course is suitable for people who:

• Data scientists.
• Machine learning engineers.
• Statisticians.

Course details for Causal Inference for Data Science

• Publisher: Oreilly
• Instructor: Aleix Ruiz de Villa
• Training level: Beginner to advanced
• Training duration: 12 hours and 41 minutes

Course headings

• Part 1. Inference and the role of confounders
• Chapter 1. Introducing causality
• Chapter 2. First steps: Working with confounders
• Chapter 3. Applying causal inference
• Chapter 4. How machine learning and causal inference can help each other
• Part 2. The adjustment formula in practice
• Chapter 5. Finding comparable cases with propensity scores
• Chapter 6. Direct and indirect effects with linear models
• Chapter 7. Dealing with complex graphs
• Chapter 8. Advanced tools with the DoubleML library
• Part 3. Other strategies beyond the adjustment formula
• Chapter 9. Instrumental variables
• Chapter 10. Potential outcomes framework
• Chapter 11. The effect of a time-related event

Course images

Sample course video

Download link

Download Part 1 – 1 GB

Download Part 2 – 723 MB

File(s) password: www.downloadly.ir

File size

1.7 GB