Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Authors: Kwangho Kim, Jose R Zubizarreta
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 23,570 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,460 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 bĪ using (b P) with K = 2 splits, under δ = (i.e., with no fairness constraints) and δ = 0. |