A Linear Algebraic Framework for Counterfactual Generation
Authors: Jong-Hoon Ahn, Akshay Vashist
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using simulated LDL cholesterol datasets, we show that our method significantly outperforms the most cited methods of counterfactual generation. |
| Researcher Affiliation | Industry | Jong-Hoon Ahn & Akshay Vashist Otsuka Pharmaceutical Development & Commercialization, Inc. Princeton, NJ 08540, USA {jong-hoon.ahn, akshay.vashist}@otsuka-us.com |
| Pseudocode | No | The paper provides mathematical derivations and equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code or a link to a code repository for the proposed methodology. |
| Open Datasets | Yes | To evaluate our counterfactual generation method provided by Theorem 2.3, we used the simulated LDL cholesterol dataset that had been used to evaluate the Sync Twin algorithm (Qian et al., 2021). |
| Dataset Splits | No | The paper mentions training data sizes (e.g., 'a dataset of N0 = N1 = 200 and a dataset of N0 = 1000 and N1 = 200') and refers to a 'test dataset', but it does not explicitly provide details for training, validation, and test dataset splits or percentages. |
| 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 does not list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | To produce Table 1 as a reproduction of Table 2 of the Sync Twin paper (Qian et al., 2021), we trained our model with K0 = K1 = 2 and M = 85 from a dataset of N0 = N1 = 200 and a dataset of N0 = 1000 and N1 = 200. We also introduced confounding bias denoted by pn for the n-th patient: pn = p0 for ωn = 0 and pn = 1 for ωn = 1. The constant p0 controls the degree of confounding bias (smaller p0, larger bias). |