Use this demo to explore the DAME and FLAME algorithms with one of our sample datasets. More datasets
and features are coming soon!
**dame-flame** is a **Python** package for performing *matching* for *observational causal inference* on datasets containing discrete covariates. It implements the *Dynamic Almost Matching Exactly (DAME)* and *Fast, Large-Scale Almost Matching Exactly (FLAME)* algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on.