Generalized Adjustment Under Confounding and Selection Biases
Authors: Juan Correa, Jin Tian, Elias Bareinboim
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment. We further design an algorithm for listing all admissible adjustment pairs in polynomial delay... Finally, we describe a statistical estimation procedure that can be performed once a set is known to be admissible... |
| Researcher Affiliation | Academia | Juan D. Correa Computer Science Department Purdue University correagr@purdue.edu Jin Tian Department of Computer Science Iowa State University jtian@iastate.edu Elias Bareinboim Computer Science Department Purdue University eb@purdue.edu |
| Pseudocode | Yes | Algorithm 1 Routines used to list admissible pairs |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with specific datasets. While it discusses the use of "unbiased data" within its theoretical framework (e.g., "leveraging external data that may be available without selection bias (e.g., data from census)"), it does not provide concrete access information (links, DOIs, specific citations) for any publicly available or open dataset used in its own research. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments that would require specific training/test/validation dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical and algorithmic contributions and does not describe any computational experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs. It does not mention any specific software components with version numbers that would be required to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments that would require specific setup details, hyperparameters, or training configurations. |