Proportion-based Sensitivity Analysis of Uncontrolled Confounding Bias in Causal Inference
Authors: Haruka Yoshida, Manabu Kuroki
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate the applicability of the PSA through two case studies. In addition, we provide a medical case study from the Cooperative Cardiovascular Project [Mac Lehose et al., 2005] and an industrial case study on setting up painting conditions of car bodies [Kuroki, 2008; Kuroki, 2012] to discuss the applicability of the PSA. |
| Researcher Affiliation | Academia | Haruka Yoshida , Manabu Kuroki Graduate School of Engineering Science, Yokohama National University yoshida-haruka-kt@ynu.jp, kuroki-manabu-zm@ynu.ac.jp |
| Pseudocode | No | The paper does not include any pseudocode or clearly labeled algorithm blocks. It presents mathematical formulations and theoretical examples. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Case Study: Cooperative Cardiovascular Project We illustrate our results by using data from the Cooperative Cardiovascular Project [Mac Lehose et al., 2005]. Table 1 shows the use of beta-blockers and 30-day mortality among acute myocardial infarction patients, stratified by ethnicity (black and white patients). |
| Dataset Splits | No | The paper does not provide specific details on how the datasets used in the case studies (Cooperative Cardiovascular Project, Study of Setting Up Coating Conditions) were split into training, validation, or test sets. It refers to 'observed data' but no specific percentages or sample counts for splits are given. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct the numerical examples or case studies. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers used for the implementation or analysis, such as programming languages, libraries, or specialized software. |
| Experiment Setup | No | The paper discusses theoretical frameworks and their application to case studies using existing datasets. It does not describe an 'experimental setup' in terms of hyperparameter values, training configurations, model initialization, or system-level settings typically found in machine learning experiments. The 'setting up coating conditions' is part of a case study's context, not the setup for their proposed method. |