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

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data