Unraveling Confounding and Associations

In this chapter, we deepen our knowledge of causal inference, exploring more complex aspects of the theory, including an overview of treatment effects. We also clarify the often-muddled concepts of confounding and associations, using real-world examples to illustrate how associations are frequently misinterpreted as causality. We introduce a mathematical framework designed to clearly distinguish between confounding, associations, and causality.

A key distinction is drawn between statistical and causal inference, particularly in the context of infinite data. In addition, we discuss two common strategies to mitigate confounding and highlight various biases inherent in causal analysis. Alright, we are all set to explore these intricate concepts in detail.

The following are the topics covered in this chapter:

• A deep dive into associations
• Causality and a fundamental issue
• The distinction between confounding and associations
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