almost-matching-exactly Goto Github PK
Name: Almost Matching Exactly Lab
Type: Organization
Name: Almost Matching Exactly Lab
Type: Organization
Code for matching with adaptively binned continuous covariates
AHB (Adaptive Hyper-box) R package provides two main algorithms: AHB_fast_match and AHB_MIP_match for Interpretable Individualized Treatment Effect Estimation.
Place to host documentation and/or webpage things
Matching methods for treatment effect estimation under network interference
Experimental code for the DAME paper. For the Python package, see: https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package
A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data
Test suite ensuring the R:FLAME package and Python dame-flame package provide the same results. For the R package, see https://github.com/almost-matching-exactly/R-FLAME. For the Python package see https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package
Experimental code for the FLAME paper. For the open source Python Package, see https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package.
Code and supplement for "Almost Matching Exactly With Instrumental Variables" (UAI 2019)
Code for Safe and Interpretable Estimation of Optimal Treatment Regimes AISTATS 2024 Paper
R Implementation of FLAME, DAME, and other algorithms in the AME framework
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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Data-Driven Documents codes.
China tencent open source team.