A Python package for performing matching for observational causal inference on datasets containing discrete covariates
Documentation here
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.
dame-flame requires Python version (>=3.6). Install from here if needed.
- NumPy (>= 1.6.1)
- Pandas (>=0.11.0)
- Scikit learn (>=0.23.2)
- SciPy (>=0.14)
If your python version does not have these packages, install from here.
To run the examples in the examples folder (these are not part of the package), Jupyter Notebooks or Jupyter Lab (available here) and Matplotlib (>=2.0.0) is also required.
Download from PyPi via $ pip install dame-flame