#Potential Outcomes

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

1 post tagged with potential outcomes. ← All posts

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Pratik Dhanave · ·6 min read

Potential Outcomes — Defining a Causal Effect Precisely

To reason about causation rigorously, you first need to define what a "causal effect" even is — and the answer is more subtle than it looks. The potential-outcomes framework defines it through a counterfactual: the difference between what happened and what would have happened otherwise. That single idea, and the "fundamental problem" that you can never observe both, is the foundation on which all of causal inference is built.

To reason about causation you must define a causal effect: the counterfactual difference between what happened and what would have happened otherwise. The potential-outcomes framework, the fundamental problem (you can never observe both), and why everything reduces to making treated and untreated groups comparable.

#Causal Inference#Potential Outcomes#Counterfactuals#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.