JanTeichertKluge/DMLSim
This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.
This library helps quantitative researchers and data scientists evaluate causal relationships using advanced machine learning. You can input various data sources, including complex text and image data, along with defined causal models, and it outputs robust average treatment effects and performance metrics for your simulations. It's designed for those who need to rigorously test causal inference models.
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Use this if you are a researcher or data scientist needing to conduct simulation studies for causal machine learning models, especially when working with partially linear regression or interactive regression models, or when incorporating unstructured text and image data.
Not ideal if you are looking for a simple, off-the-shelf causal inference solution without the need for extensive simulation or integrating complex deep learning embeddings.
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Last pushed
Jun 20, 2023
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