CyberAgentAILab/python-dte-adjustment
dte_adj is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils.
This is a Python package for data scientists, statisticians, and researchers who need to precisely estimate how different interventions or "treatments" affect the entire distribution of an outcome, not just the average. It takes experimental data (e.g., A/B test results) and outputs detailed distribution functions, helping you understand the full range of impacts. You would use this if you're analyzing randomized experiments and want to move beyond simple average comparisons.
Use this if you need to understand the full spectrum of effects from an intervention, not just the average change, especially when dealing with experimental or causal inference data.
Not ideal if you are only interested in estimating average treatment effects or if your data does not come from a randomized experiment.
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License
MIT
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Last pushed
Mar 09, 2026
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