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Causal analysis overview: Causal inference versus experimentation versus causal discovery

Causal analysis overview: Causal inference versus experimentation versus causal discovery

An introductory overview of causal analysis describing three methodologies used to generate causal insights to power data-driven decision making

https://medium.com/data-science-at-microsoft/causal-analysis-overview-causal-inference-versus-experimentation-versus-causal-discovery-d7c4ca99e3e4

“Here’s what this article covers:

  • Understanding causal analysis: What it is and why it’s important.
  • Causal questions: Types of causal questions and how to answer them.
  • Causal discovery: How to derive causal structures from data.
  • Causal inference: (i) Experimentation: The role of randomized control trials (RCTs) and clinical trials. (ii) Non-experimentation causal inference: Estimating causal effects using observational data.
  • Comparing methodologies: When to use each approach for different causal questions.”

 

 

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Posted on: January 24, 2025, 6:41 am Category: Uncategorized

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