Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Authors: Kwangho Kim, Jose R Zubizarreta
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the methods in a simulation study and illustrate them in a real-world case study. |
| Researcher Affiliation | Academia | 1Department of Statistics, Korea University, Seoul, South Korea 2Department of Health Care Policy, Harvard Medical School, MA, USA 3Departments of Biostatistics and Statistics, Harvard University, MA, USA. |
| Pseudocode | No | The paper describes the estimation steps in prose and mathematical formulations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We next illustrate our methods on the COMPAS dataset originally gathered to assess the risk of recidivism (Angwin et al., 2016). |
| Dataset Splits | Yes | We use roughly two-thirds of the data to estimate bg, and the rest to estimate the welfare and unfairness using the same setting as in the preceding subsection. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | All nuisance functions are estimated using the cross-validation super learner ensemble implemented in the Super Learner R package to combine generalized additive models, adaptive regression splines, and random forests. (Specific version numbers for the R package or other libraries are not provided). |
| Experiment Setup | Yes | b(W) consists of the polynomial terms W, W 2, W 3 and {Wj Wk Ws}j,k,s to form the third-order Taylor expansion. All nuisance functions are estimated using the cross-validation super learner ensemble implemented in the Super Learner R package to combine generalized additive models, adaptive regression splines, and random forests. ... estimate bτ using (b P) with K = 2 splits, under δ = (i.e., with no fairness constraints) and δ = 0. |