Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
Authors: Nathan Kallus, Angela Zhou
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate all of this in a case study of personalized job training based on a dataset from a French field experiment. ... In Fig. 1, we plot the identified disparity curves of Eq. (5) corresponding to the maximal and minimal sensitivity bounds on TPR and TNR disparity between groups. ... We learn by the Generalized Random Forests method of [5, 64] and use sample splitting, learning on half the data and using our methods to assess bounds on TPR, TNR and other quantities with out-of-sample estimates on the other half of the data. We bootstrap over 50 sampled splits and average disparity curves to reduce sample uncertainty. |
| Researcher Affiliation | Academia | Nathan Kallus Cornell University New York, NY kallus@cornell.edu Angela Zhou Cornell University New York, NY az434@cornell.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that its code is publicly available. |
| Open Datasets | Yes | We consider a case study from a three-armed large randomized controlled trial that randomly assigned job-seekers in France to a control-group, a job training program managed by a public vendor, and an out-sourced program managed by a private vendor [11]. |
| Dataset Splits | Yes | We learn by the Generalized Random Forests method of [5, 64] and use sample splitting, learning on half the data and using our methods to assess bounds on TPR, TNR and other quantities with out-of-sample estimates on the other half of the data. We bootstrap over 50 sampled splits and average disparity curves to reduce sample uncertainty. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using the "Generalized Random Forests method of [5, 64]" but does not specify version numbers for any software or libraries used. |
| Experiment Setup | No | The paper mentions using Generalized Random Forests and sample splitting but does not provide specific experimental setup details such as hyperparameters or training configurations. |