Practice Causal Discovery (10.2.3) - Causality & Domain Adaptation - Advance Machine Learning
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Causal Discovery

Practice - Causal Discovery

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define causal discovery in your own words.

💡 Hint: Think about the difference between correlation and causation.

Question 2 Easy

What does the PC algorithm stand for?

💡 Hint: What type of algorithm is this?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main goal of causal discovery?

To identify correlations
To determine causal relationships
To classify data

💡 Hint: Remember the difference between correlation and causation.

Question 2

True or False: Score-based methods do not rely on measuring independence.

True
False

💡 Hint: Consider what each method relies on for causal discovery.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have data from two cities regarding air pollution levels and respiratory illness rates. Describe how you might use PC and GES differently to analyze the data for causal relationships.

💡 Hint: Consider the strengths of each method.

Challenge 2 Hard

Design a mini-research study attempting to use LiNGAM for causal discovery in social sciences, outlining the assumptions you need to verify.

💡 Hint: Reflect on the assumptions necessary for model validity.

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Reference links

Supplementary resources to enhance your learning experience.