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EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal to combine state-of-the-art...
Causal Inference 360
A Python package for inferring causal effects from observational data.
Causal inference analysis enables estimating the causal effect of an intervention on some outcome from real-world non-experimental observational data.
This package provides...
1. Installing the package
2. Creating an example dataset
3. Running an analysis
4. Plotting the results
5. Working with dates and times
6. Printing a summary table
7. Adjusting the model
8. Using a custom model
10. Further resources
An R package...
Causality and Machine Learning
At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying...
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