Recovering Causal Effects from Selection Bias
Authors: Elias Bareinboim, Jin Tian
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide graphical and algorithmic conditions for recoverability of interventional distributions for when selection and confounding biases are both present. Our treatment completely characterizes the class of causal effects that are recoverable in Markovian models, and is sufficient for Semi-Markovian models. |
| Researcher Affiliation | Academia | Elias Bareinboim Computer Science Department University of California, Los Angeles Los Angeles, CA. 90095 eb@cs.ucla.edu Department of Computer Science Iowa State University Ames, IA. 50011 jtian@iastate.edu |
| Pseudocode | Yes | Figure 4: Algorithm based on c-components capable of simultaneously identifying and recovering causal effects. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets, therefore no training data or access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies with datasets, therefore no validation splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. |