Individual skill
modeling-strategy-guide
Strategic statistical modeling, experimentation, and causal inference
skills/analysis/statistics/modeling-strategy-guide/SKILL.md
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# Modeling Strategy Guide
A skill for strategic statistical modeling applied to academic research. Covers advanced modeling decisions, experimental design, causal inference, feature engineering, and the critical thinking required to move from data to defensible conclusions.
## Overview
Senior data scientists distinguish themselves not by knowing more algorithms but by asking better questions, designing cleaner experiments, and being honest about what the data can and cannot tell them. This skill translates that professional discipline into a research context, helping academics apply modern data science practices to their empirical work. It covers the strategic decisions that matter most: when to use simple models versus complex ones, how to establish causality rather than mere correlation, and how to communicate uncertainty honestly.
The skill is particularly useful for researchers working with observational data who need causal inference techniques, those designing randomized experiments who need proper power calculations and analysis plans, and anyone building predictive models who needs to avoid common overfitting and leakage pitfalls.
## Strategic Modeling Decisions
### Model Selection Philosophy
