Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
Authors: Ilya Shpitser
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our approach with a simple simulation study, and an analysis of risk of premature birth in women in Botswana exposed to highly active anti-retroviral therapy. |
| Researcher Affiliation | Academia | Ilya Shpitser Department of Computer Science Johns Hopkins University ilyas@cs.jhu.edu |
| Pseudocode | No | The paper describes the estimation procedure conceptually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the methodology, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | To illustrate the performance of our model in a practical setting where data is missing not at random, we report an analysis of a survey dataset for HIV-infected women in Botswana, also analyzed in [18]. |
| Dataset Splits | No | The paper describes generating datasets for simulation and using a real-world dataset but does not specify exact percentages, sample counts, or citations to predefined splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the data generation process for the simulation study (e.g., multivariate normal distribution parameters, sample sizes) and the use of bootstrap for confidence intervals, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes) or training schedules relevant to model optimization. |