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AME: Interpretable Almost Exact Matching for Causal Inference
Haoning Jiang · Thomas Howell · Neha Gupta · Vittorio Orlandi · Sudeepa Roy · Marco Morucci · Harsh Parikh · Alexander Volfovsky · Cynthia Rudin

Wed Dec 08 09:05 AM -- 09:20 AM (PST) @
Event URL: https://almost-matching-exactly.github.io/ame-demo »

AME (Almost Matching Exactly) is an interactive web-based application that allows users to perform matching for observational causal inference on large datasets. The AME application is powered by Fast Large-Scale Almost Matching Exactly (FLAME) (JMLR’21) and Dynamic Almost Matching Exactly (DAME) (AISTATS’19) algorithms that match treatment and control units in a way that is interpretable, because the matches are made directly on covariates, high-quality, because machine learning is used to determine which covariates are important to match on, and scalable, using techniques from data management. Our demonstration shows the usefulness of these algorithms and allows easy interactive explorations for treatment effect estimates and corresponding matched groups of units with a suite of visualizations providing detailed insights to users.

Author Information

Haoning Jiang (Duke University)
Thomas Howell (Duke University)
Neha Gupta (Duke University)
Vittorio Orlandi (Duke University)
Sudeepa Roy (Duke University, USA)
Marco Morucci (Duke University)
Harsh Parikh (Duke University)
Alexander Volfovsky (Duke University)
Cynthia Rudin (Duke)

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