#Propensity Score

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

1 post tagged with propensity score. ← All posts

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Pratik Dhanave · ·7 min read

Observational Methods — Causal Inference Without an Experiment

Often you can't run an experiment — it's unethical, infeasible, or too slow — but you still need to know whether something causes something. Observational causal inference is the set of techniques for extracting causal conclusions from data you didn't control, by reconstructing the comparable groups that randomization would have given you for free. This post covers the main tools (adjustment, matching, propensity scores) and the uncomfortable assumption they all depend on.

When you can't experiment, observational methods reconstruct the comparable groups randomization gives for free — adjustment, matching, propensity scores. All share one goal (remove confounders' influence) and one unprovable Achilles' heel: the 'no unmeasured confounding' assumption. How to use them honestly.

#Causal Inference#Propensity Score#Observational Studies#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.