dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the Dynamic Almost Matching Exactly (DAME) and Fast Large-Scale Almost Matching Exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made on covariates (rather than, for instance, propensity scores), and high-quality, because machine learning is used to determine which covariates are important to match on. DAME solves an optimization problem that matches units on as many covariates as possible, prioritizing matches on important covariates. FLAME approximates the solution found by DAME via a much faster backward feature selection procedure. The package provides several adjustable parameters to adapt the algorithms to specific applications, and can calculate treatment effects after matching. Descriptions of these parameters, details on estimating treatment effects, and further examples, can be found in the documentation at https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/

中文翻译：

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dame-flame：一个提供因果推理的快速可解释匹配的Python库

dame-flame是一个Python软件包，用于对包含离散协变量的数据集进行观测因果推断匹配。该程序包实现了“动态几乎完全匹配”（DAME）和“快速大规模几乎完全匹配”（FLAME）算法，它们在协变量的子集上匹配处理单元和控制单元。产生的匹配组是可解释的，因为匹配是在协变量（而不是倾向得分）上进行的，因此是高质量的，因为使用机器学习来确定匹配哪些重要的协变量。DAME解决了一个优化问题，该问题可以在尽可能多的协变量上匹配单元，从而对重要协变量上的匹配进行优先排序。FLAME通过更快的后向特征选择过程来近似DAME找到的解决方案。该软件包提供了几个可调参数，以使算法适应特定应用，并可以在匹配后计算治疗效果。这些参数的描述，估计治疗效果的详细信息以及更多示例可以在https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/的文档中找到。