It does so by partitioning the dataset along axis-aligned cuts, at each stage choosing to cut a partition into the two partitions with least sum of impurities ...
We propose a new approach based on recursively partitioning the data into regimes where different treatments are optimal.
Aug 31, 2016 · We propose a new approach based on recursively partitioning the data into regimes where different treatments are optimal.
Abstract. We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for.
We develop new tools for validating and evaluating personalization models on observational data and use these to demonstrate the power of our novel approaches ...
This work proposes a new approach based on recursively partitioning the data into regimes where different treatments are optimal, extending this approach to ...
Jul 1, 2017 · We study the problem of learning to choose from m m discrete treatment options (e.g., news item or medical drug) the one with best causal ...
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What is the recursive partitioning method?
This project reproduces the paper "Recursive Partitioning for Personalization using Observational Data". Citation and the Original Paper. Kallus, N. (2017, July) ...
We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects.
Recursive partitioning ... We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects.