Notice: 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. Full accuracy metrics and methodology are described in [1].
Identifying Causal Effects Under Functional Dependencies
Authors: Yizuo Chen, Adnan Darwiche
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects. |
| Researcher Affiliation | Academia | Yizuo Chen Department of Computer Science University of California, Los Angeles Los Angeles, CA 90024 EMAIL Adnan Darwiche Department of Computer Science University of California, Los Angeles Los Angeles, CA 90024 EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Procedural steps are described in paragraph form. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. It is a theoretical paper without associated software release. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments involving datasets. Examples (e.g., Figure 1) are illustrative, not empirical. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments; therefore, it does not provide details about experimental setup, hyperparameters, or training configurations. |