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Algorithm Overview

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

Table of contents
  1. Dynamic Almost Matching Exactly (DAME)
  2. Fast Large-Scale Almost Matching Exactly (FLAME)
  3. Matching After Learning to Stretch (MALTS)
  4. Adaptive Hyper-Box Matching (AHB)

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 (continous 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 (continous 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