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Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise., , , and . CAMSAP, page 699-703. IEEE, (2019)Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference., , , , and . CoRR, (2018)Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference., , , , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 3252-3262. PMLR, (2020)dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference., , , , , , , , , and 2 other author(s). CoRR, (2021)MALTS: Matching After Learning to Stretch., , and . J. Mach. Learn. Res., (2022)MALTS: Matching After Learning to Stretch., , and . CoRR, (2018)Estimating Trustworthy and Safe Optimal Treatment Regimes., , , , , , and . CoRR, (2023)Likelihoods for fixed rank nomination networks., , , and . Netw. Sci., 1 (3): 253-277 (2013)Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference., , , , , , , , and . AAAI, page 22331-22340. AAAI Press, (2024)Safe and Interpretable Estimation of Optimal Treatment Regimes., , , , , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 2134-2142. PMLR, (2024)