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cs.LG2024
Sparse and Faithful Explanations Without Sparse Models
Yiyang Sun, Zhi Chen, Vittorio Orlandi +2
Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an…
cs.LG2021★ 3 cited
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang +9
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the Dynamic Almost Mat…