Segregated Graphs and Marginals of Chain Graph Models
Authors: Ilya Shpitser
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the utility of segregated graphs for analyzing outcome interference in causal inference via simulated datasets. |
| Researcher Affiliation | Academia | Ilya Shpitser Department of Computer Science Johns Hopkins University ilyas@cs.jhu.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement) to open-source code for the described methodology. |
| Open Datasets | No | The paper states: 'We generated 1000 members of our family described above, used each member to generate 5000 samples'. It describes a data generation process but does not provide access to a publicly available or open dataset, nor a link to the generated data. |
| Dataset Splits | No | The paper mentions generating samples and fitting models but does not specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory, or cloud resources). |
| Software Dependencies | No | The paper mentions using 'an approach described in [5]' for fitting the model, but it does not list specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | In all members of this family, A was assigned via a fair coin, p(W | A, U) was a logistic model with no interactions, B1 was randomly assigned via a fair coin given no complications (W = 1), otherwise B1 was heavily weighted (0.8 probability) towards treatment assignment. The distribution p(Y1, Y2 | U, B1, A) was obtained from a joint distribution p(Y1, Y2, U, B1, A) in a log-linear model of an undirected graph G of the form: 1 C( 1) x C 1λC , where C ranges over all cliques in G, . 1 is the L1-norm, λC are interactions parameters, and Z is a normalizing constant. In our case G was an undirected graph over A, B1, U, Y1, Y2 where edges from Y2 to B1 and U were missing, and all other edges were present. Parameters λC were generated from N(0, 0.3). |