Causal Fairness for Outcome Control
Authors: Drago Plecko, Elias Bareinboim
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the causal framework of outcome control to the problem of allocating mechanical ventilation in intensive care units (ICUs)...To investigate this issue using the tools developed in this paper, we use the data from the MIMIC-IV dataset [17, 16]...The learning rate was fixed at η = 0.1, and the optimal number of rounds was chosen via 10-fold cross-validation. We then use the obtained model to generate predictions...The results for the probability of treatment given a fixed decile are shown in Fig. 6b. |
| Researcher Affiliation | Academia | Drago Plecko and Elias Bareinboim Department of Computer Science Columbia University dp3144@columbia.edu, eb@cs.columbia.edu |
| Pseudocode | Yes | Algorithm 1 Decision-Making with Benefit Fairness; Algorithm 2 Benefit Fairness Causal Explanation; Algorithm 3 Causal Discrimination Removal for Outcome Control |
| Open Source Code | Yes | The source code for reproducing all the experiments can be found in our code repository. Furthermore, the vignette accompanying the main text can be found here. |
| Open Datasets | Yes | To investigate this issue using the tools developed in this paper, we use the data from the MIMIC-IV dataset [17, 16] that originates from the Beth Israel Deaconess Medical Center in Boston, Massachusetts...[17] A. E. Johnson, L. Bulgarelli, L. Shen, A. Gayles, A. Shammout, S. Horng, T. J. Pollard, B. Moody, B. Gow, L.-w. H. Lehman, et al. Mimic-iv, a freely accessible electronic health record dataset. Scientific data, 10(1):1, 2023. |
| Dataset Splits | Yes | The learning rate was fixed at η = 0.1, and the optimal number of rounds was chosen via 10-fold cross-validation. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., CPU, GPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an |
| Experiment Setup | Yes | We fit an xgboost model which regresses Y on D, X, Z, and W, to obtain the fit b Y . The learning rate was fixed at η = 0.1, and the optimal number of rounds was chosen via 10-fold cross-validation. |