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causal-inference

Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.

skills/causal-inference/SKILL.md

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# Causal Inference

Correlation is cheap; causation is the question that matters for decisions. Causal inference is the 
toolkit for answering "what would happen if we did X?" — from experiments when possible, and from 
observational data with explicit assumptions when not.

## Overview

Every causal claim rests on assumptions — the craft is making them explicit and testing them. 
Draw the causal graph (DAG) to see what confounds what. Choose the identification strategy that 
fits: randomization, or quasi-experiments (difference-in-differences, regression discontinuity, 
instrumental variables), or adjustment with careful confounder selection. Then probe robustness: 
sensitivity analysis asks how wrong the assumptions would need to be to overturn the conclusion.