Salesforce CausalAI Library: A Fast and Scalable Framework for Causal
Analysis of Time Series and Tabular Data
- URL: http://arxiv.org/abs/2301.10859v2
- Date: Sat, 23 Sep 2023 00:30:22 GMT
- Title: Salesforce CausalAI Library: A Fast and Scalable Framework for Causal
Analysis of Time Series and Tabular Data
- Authors: Devansh Arpit, Matthew Fernandez, Itai Feigenbaum, Weiran Yao,
Chenghao Liu, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang,
Stephen Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles
- Abstract summary: We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data.
The goal of this library is to provide a fast and flexible solution for a variety of problems in the domain of causality.
- Score: 76.85310770921876
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 discrete, continuous and
heterogeneous 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 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}.
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