#Causal Graphs

Articles about Causal Graphs — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with causal graphs. ← All posts

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Pratik Dhanave · ·7 min read

Causal Graphs — Drawing What You Believe Causes What

Causal reasoning gets dramatically clearer when you draw it. A causal graph is a diagram of what causes what — nodes for variables, arrows for causal influence — and it turns fuzzy debates about confounding and adjustment into visual rules you can actually check. This post introduces causal graphs (DAGs) and the single most useful thing they do: tell you exactly which variables to control for, and which ones you must leave alone.

Causal reasoning gets clearer when you draw it. A causal graph (DAG) turns fuzzy debates about confounding and adjustment into visual rules: the three building blocks (chain/mediator, fork/confounder, collider) and why 'control for everything' is wrong — adjusting the wrong variable adds bias.

#Causal Inference#Causal Graphs#DAG#Statistics#Data Science

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.