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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
Authors: Yonghan Jung, Min Woo Park, Sanghack Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we exemplify this incompleteness and then present the sequential adjustment criterion, a sound and complete criterion for sequential covariate adjustment. We provide a constructive sequential adjustment criterion that identifies a set that satisfies the sequential adjustment criterion if and only if the causal effect can be expressed as a sequential covariate adjustment. Finally, we present an algorithm for identifying a minimal sequential covariate adjustment set, which optimizes efficiency by ensuring that no unnecessary vertices are included. |
| Researcher Affiliation | Academia | Purdue University Seoul National University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: min SCA(X, Y, G) |
| Open Source Code | Yes | The code is available at https://github.com/snu-causality-lab/minSAC |
| Open Datasets | No | This paper does not include any data analysis or experimental results, and thus no datasets are used for training. |
| Dataset Splits | No | This paper does not include any data analysis or experimental results, and thus no dataset splits for validation are discussed. |
| Hardware Specification | No | This paper focuses on theoretical contributions and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This paper focuses on theoretical contributions and does not list any specific software dependencies with version numbers for experiments. |
| Experiment Setup | No | This paper focuses on theoretical contributions and does not describe an experimental setup with hyperparameters or training settings. |