Causal Inference Project
Use causal inference methods (DiD, propensity matching, IV) on a real-world observational dataset. Senior-level signal.
PythonEconML or DoWhypandasstatsmodels
About this project
Causal inference is the next-level data science skill — going beyond "X is correlated with Y" to "X caused Y". This project teaches Difference-in-Differences, propensity score matching, and instrumental variables. Use a public dataset (e.g., minimum wage effects, public health data) and write a methodologically-rigorous analysis.
Why build this in 2026?
Causal inference is one of the few advanced quant skills not eaten by LLMs — judgement-heavy.
What you'll ship
- Jupyter notebook
Writeup explaining method choice and limitations
Code reproducible from a single command
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Skills you'll practice
pythonstatisticsmachine learning