dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
This library addresses the problem of interpretable and high-quality causal inference for researchers and practitioners working with observational data and discrete covariates, offering a practical tool for applying established algorithms.
This paper introduces dame-flame, a Python library for causal inference using DAME and FLAME algorithms. It provides interpretable and high-quality matching for observational studies with discrete covariates, allowing for the estimation of treatment effects.
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package 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 solves an optimization problem that matches units on as many covariates as possible, prioritizing matches on important covariates. FLAME approximates the solution found by DAME via a much faster backward feature selection procedure. The package provides several adjustable parameters to adapt the algorithms to specific applications, and can calculate treatment effect estimates after matching. Descriptions of these parameters, details on estimating treatment effects, and further examples, can be found in the documentation at https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/