Algorithm Overview

Read the summaries below to determine which of our matching algorithms is right for you.

Dynamic Almost Matching Exactly (DAME)

Languages:Python
Input data:Categorical covariates, works best with small to moderately-sized datasets
Matching method:Uses bit-vector computations to match units based on a learned, weighted Hamming distance.
Paper:Interpretable Almost Matching Exactly for Causal Inference

Fast Large-Scale Almost Matching Exactly (FLAME)

Languages:R, Python
Input data:Categorical covariates, scales well to large datasets with millions of observations
Matching method:Uses bit-vector computations to match units based on a learned, weighted Hamming distance. FLAME successively drops irrelevant covariates to lessen the computational load while still maintaining enough covariates for high-quality conditional average treatment effect (CATE) estimation.
Paper:FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

Matching After Learning to Stretch (MALTS)

Languages:Python
Input data:Continuous, categorical, or mixed (continuous and categorical) covariates
Matching method:Uses exact matching for discrete variables and learned, generalized Mahalanobis distances for continuous variables. Instead of a predetermined distance metric, the covariates contributing more towards predicting the outcome are given higher weights.
Paper:MALTS: Matching After Learning to Stretch

Adaptive Hyper-Box Matching (AHB)

Languages:R
Input data:Continuous, categorical, or mixed (continuous and categorical) covariates
Matching method:Matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. The regions are found as either the solution to a mixed integer program, or by using a fast approximation algorithm.
Paper:Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

Model-to-Match: Lasso Coefficient Matching (LCM)

Languages:Python
Input data:Continuous covariates
Matching method:Creates almost exact matches in a computationally scalable manner, which works well for high-dimensional and/or big data. The feature importances from an outcome regression model are used as distance metric weights.
Paper:From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference