We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and time series data, of both discrete and continuous types. This library includes algorithms that handle linear and non-linear causal relationships between variables, and uses multi-processing for speed-up. We also include a data generator capable of generating synthetic data with specified structural equation model for both the aforementioned data formats and types, that helps users control the ground-truth causal process while investigating various algorithms. Finally, we provide a user interface (UI) that allows users to perform causal analysis on data without coding. The goal of this library is to provide a fast and flexible solution for a variety of problems in the domain of causality. This technical report describes the Salesforce CausalAI API along with its capabilities, the implementations of the supported algorithms, and experiments demonstrating their performance and speed. Our library is available at \url{https://github.com/salesforce/causalai}.

翻译：我们介绍 Salesforce CausalAI 库，这是一个基于观测数据进行因果分析的开源库。该库支持离散型和连续型表格数据及时间序列数据的因果发现与因果推断，包含处理变量间线性和非线性因果关系的算法，并采用多进程技术提升计算速度。我们还集成了一个数据生成器，能够针对前述两种数据格式与类型生成具有指定结构方程模型的合成数据，帮助用户在探究各类算法时控制真实因果过程。此外，我们提供用户界面（UI），允许用户无需编码即可对数据执行因果分析。本库旨在为因果领域各类问题提供快速灵活的解决方案。本技术报告阐述了 Salesforce CausalAI 应用程序接口（API）及其功能、已实现算法的具体实现，以及展示其性能与速度的实验结果。本库可通过 \url{https://github.com/salesforce/causalai} 获取。