Causation Analysis

Causation analysis is a methodological approach used to identify and understand cause-and-effect relationships between variables or events, rather than just correlations. It involves techniques to determine whether changes in one factor directly lead to changes in another, often applied in fields like data science, economics, and social sciences. This helps in making informed decisions, predicting outcomes, and designing interventions based on causal insights.

Also known as: Causal Inference, Causal Analysis, Cause-Effect Analysis, Causality, Causal Modeling

Why learn Causation Analysis?

Developers should learn causation analysis when working on projects that require understanding the impact of specific actions or variables, such as in A/B testing, policy evaluation, or machine learning model interpretability. It is crucial for building robust systems where decisions depend on causal relationships, like in recommendation algorithms or healthcare analytics, to avoid misleading correlations and ensure effective solutions.

Learning Resources

• book: Causal Inference for Statistics, Social, and Biomedical Sciences
• tutorial: Causal Inference: The Mixtape
• course: Coursera: Causal Inference
• video: Introduction to Causal Inference
• docs: DoWhy: A Python Library for Causal Inference