Full Law Identification in Graphical Models of Missing Data: Completeness Results

Authors: Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser

ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We provide the first completeness result in this field of study necessary and sufficient graphical conditions under which, the full data distribution can be recovered from the observed data distribution. We then simultaneously address issues that may arise due to the presence of both missing data and unmeasured confounding, by extending these graphical conditions and proofs of completeness, to settings where some variables are not just missing, but completely unobserved.
Researcher Affiliation Academia 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Pseudocode No The paper refers to existing "algorithms" from prior work but does not present its own pseudocode or algorithm blocks.
Open Source Code No The paper does not mention providing open-source code for the described methodology.
Open Datasets No The paper is theoretical and does not involve training models on datasets. Therefore, no information about public dataset availability is provided.
Dataset Splits No The paper is theoretical and does not involve empirical validation on datasets. Therefore, no information about training/test/validation splits is provided.
Hardware Specification No The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not describe an implementation with specific software dependencies. No software names with version numbers are mentioned.
Experiment Setup No The paper is theoretical and does not describe empirical experiments. Therefore, no experimental setup details such as hyperparameters or training configurations are provided.