On Positivity Condition for Causal Inference
Authors: Inwoo Hwang, Yesong Choe, Yeahoon Kwon, Sanghack Lee
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Against this background, we examine the graphical counterpart of the conventional positivity condition so as to license the use of identification formula without strict positivity. In particular, we explore various approaches, including analysis in a post-hoc manner, do-calculus, Q-decomposition, and algorithmic, to yielding a positivity condition for an identification formula, where we relate them, providing a comprehensive view. |
| Researcher Affiliation | Academia | 1Artificial Intelligence Institute, Seoul National University, Seoul, South Korea 2Graduate School of Data Science, Seoul National University, Seoul, South Korea. |
| Pseudocode | Yes | Algorithm 1 IDENTIFY+ (outer) Algorithm 2 IDENTIFY+ (inner) |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies or use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets, thus no dataset splits are discussed for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations and algorithms, without mentioning specific software dependencies or versions for implementation. |
| Experiment Setup | No | The paper is theoretical, presenting conceptual work and algorithms, and does not include details on experimental setup or hyperparameters. |