Identification and Estimation of Causal Effects from Dependent Data
Authors: Eli Sherman, Ilya Shpitser
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then demonstrate how statistical inference may be performed on causal parameters identiļ¬ed by this algorithm. In particular, we consider cases where only a single sample is available for parts of the model due to full interference, i.e., all units are pathwise dependent and neighbors treatments affect each others outcomes [24]. We apply these techniques to a synthetic data set which considers users sharing fake news articles given the structure of their social network, user activity levels, and baseline demographics and socioeconomic covariates. |
| Researcher Affiliation | Academia | Eli Sherman Department of Computer Science Johns Hopkins University Baltimore, MD 21218 esherman@jhu.edu Ilya Shpitser Department of Computer Science Johns Hopkins University Baltimore, MD 21218 ilyas@cs.jhu.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks (e.g., a figure or section explicitly labeled 'Algorithm' or 'Pseudocode'). |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions applying techniques to 'a synthetic data set' in the abstract and elaborates on using 'a social network is a 3-regular graph, with networks of size N = [400, 800, 1000, 2000]' in the experiments section. However, no concrete access information (link, DOI, repository, or formal citation for public availability) for this synthetic data set is provided. |
| Dataset Splits | No | The paper mentions performing '1000 bootstrap samples' for the experiments, but it does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or specialized solvers). |
| Experiment Setup | No | The paper describes the estimation strategy by adapting existing methods (auto-g-computation based on pseudo-likelihood and coding estimators) and mentions using '1000 bootstrap samples'. However, it does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or other concrete configuration parameters for reproducibility. |