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].
Identification of Causal Effects in the Presence of Selection Bias
Authors: Juan D. Correa, Jin Tian, Elias Bareinboim2744-2751
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider the problem of identifiability of causal effects when both confounding and selection biases are simultaneously present. We first investigate the problem of identifiability when all the available data is biased. We prove that the algorithm proposed by [Bareinboim and Tian, 2015] is, in fact, complete, namely, whenever the algorithm returns a failure condition, no identifiability claim about the causal relation can be made by any other method. We then generalize this setting to when, in addition to the biased data, another piece of external data is available, without bias. It may be the case that a subset of the covariates could be measured without bias (e.g., from census). We examine the problem of identifiability when a combination of biased and unbiased data is available. We propose a new algorithm that subsumes the current state-of-the-art method based on the back-door criterion. |
| Researcher Affiliation | Academia | Juan D. Correa Computer Science Department Purdue University EMAIL Jin Tian Department of Computer Science Iowa State University EMAIL Elias Bareinboim Computer Science Department Purdue University EMAIL |
| Pseudocode | Yes | Algorithm 1 Procedure in (Bareinboim and Tian 2015) for recovering Q[E]... Algorithm 2 Recursive function used to recover an arbitrary c-component... Algorithm 3 Algorithm capable of recovering Px(y) from selection bias with external data |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | No | The paper is theoretical and does not mention using any datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not mention any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not provide any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on an experimental setup, hyperparameters, or training configurations. |