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A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment. — Wikipedia

In this tutorial, we use simple datasets to illustrate two quasi-experimental methods: Propensity Score Matching (PSM) and Difference-in-differences (DID). We focus on the practical side of applying the methods and provide code in both Python and R via Kaggle (see links below).

This is joint work with my friend Dr. Gang Wang.

Read the full tutorial at

Originally published at on June 1, 2022.