causalml and doubleml-for-py
These are complements: CausalML provides a broader toolkit for uplift modeling and heterogeneous treatment effects across multiple algorithms, while DoubleML specializes in the double/debiased machine learning framework—a specific methodological approach that could be integrated into or used alongside CausalML's pipeline for more rigorous causal inference.
About causalml
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
This project helps marketers and data analysts understand the true impact of different actions on customer behavior. By analyzing experimental or historical data, it tells you which specific customers are most likely to respond positively to an ad campaign or a personalized product recommendation. The output is a clear estimate of how each individual customer will react to an intervention.
About doubleml-for-py
DoubleML/doubleml-for-py
DoubleML - Double Machine Learning in Python
This tool helps researchers and analysts determine causal effects more reliably by combining traditional econometrics with modern machine learning. You provide your dataset, specify the treatment and outcome variables, and it generates robust estimates of how one factor influences another, even in complex situations. It's designed for quantitative researchers, data scientists, and anyone needing to draw strong causal conclusions from observational data.
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