causlib

causlib is a software library designed to facilitate causal inference. It provides tools and algorithms for estimating causal effects from observational data, a common challenge in many fields. The library aims to make advanced causal inference techniques more accessible to researchers and practitioners who may not have specialized expertise in econometrics or statistics.

The core functionality of causlib revolves around implementing various causal inference methods. These typically include techniques such as matching, inverse probability weighting, regression adjustment, and more sophisticated approaches like doubly robust estimators. The library often supports the creation and analysis of causal diagrams, which are graphical representations of causal relationships between variables. This helps users to explicitly define their causal assumptions and to identify potential confounders.

causlib's development is often driven by the need for reproducible research in causal inference. By providing a standardized set of tools, it allows researchers to share their analytical workflows and to replicate results more easily. The library may be implemented in popular programming languages like Python or R, leveraging existing data science ecosystems. Its target audience includes data scientists, statisticians, economists, social scientists, and anyone seeking to understand cause-and-effect relationships from data.