Optimal and Fair Encouragement Policy Evaluation and Learning
Authors: Angela Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments", "Our case study is on a dataset of judicial decisions on supervised release based on risk-score-informed recommendations [38].", "In Figure 1 we provide descriptive information illustrating heterogeneity (including by protected attribute) in adherence and effectiveness.", "In Figure 2 we highlight results from constrained policy optimization. |
| Researcher Affiliation | Academia | Angela Zhou Department of Data Sciences and Operations University of Southern California zhoua@usc.edu |
| Pseudocode | Yes | Algorithm 1 MW2REDFAIR(D, g, E, M, d)", "Algorithm 2 Two-stage localized fair classification via reductions |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Our case study is on a dataset of judicial decisions on supervised release based on risk-score-informed recommendations [38].", "The Oregon Health Insurance Study [16] is an important study on the causal effect of expanding public health insurance on healthcare utilization, outcomes, and other outcomes. |
| Dataset Splits | Yes | Algorithm 2 Two-stage localized fair classification via reductions 1: Randomly split the data into D1, D2", "We split the data, learn nuisance estimators η1 for use in our policy value and constraint estimates, and run Algorithm 1 (MW2REDFAIR(D1, h, E, M, d; η1)) to obtain an estimate of the optimal policy ˆπ1, and the constraint variances at ˆπ1. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions the use of 'logistic regression' and 'gradient-boosted regression' but does not specify any software or library names with version numbers that would be required for reproducibility. |
| Experiment Setup | No | The paper mentions the types of models used (logistic regression, gradient-boosted regression) and arbitrarily set cost values for the objective function but does not provide specific training hyperparameters such as learning rates, batch sizes, or optimizer settings for these models. |