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
“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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