Identifying Causal Effects via Context-specific Independence Relations
Authors: Santtu Tikka, Antti Hyttinen, Juha Karvanen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented the search in C++ and the code is available in the R-package dosearch on CRAN [31]. First we will present a simulation study on the search and then show a host of examples where identifiability can be shown with our approach. Experiments were performed on a modern desktop computer (single thread, Intel Core i7-4790, 3.4 GHz). We considered DAGs with n = 7, 8, 9 nodes with 100 DAGs for each n. Edges for the DAGs were sampled randomly with average degree of 3. |
| Researcher Affiliation | Academia | Santtu Tikka Department of Mathematics and Statistics University of Jyvaskyla, Finland santtu.tikka@jyu.fi Antti Hyttinen HIIT, Department of Computer Science University of Helsinki, Finland antti.hyttinen@helsinki.fi Juha Karvanen Department of Mathematics and Statistics University of Jyvaskyla, Finland juha.t.karvanen@jyu.fi |
| Pseudocode | Yes | Algorithm 1 Input: Target Q = P(Y | do(X), Z), LDAG G and input I = {P(W )}. Output: A formula F for Q in terms of P(W ) or NA. |
| Open Source Code | Yes | We implemented the search in C++ and the code is available in the R-package dosearch on CRAN [31]. |
| Open Datasets | No | The paper describes generating synthetic DAGs ("We considered DAGs with n = 7, 8, 9 nodes with 100 DAGs for each n. Edges for the DAGs were sampled randomly with average degree of 3."), but does not provide concrete access information (link, DOI, specific repository, or formal citation to an established public dataset) for this data. |
| Dataset Splits | No | The paper describes a simulation study on randomly sampled DAGs but does not provide specific dataset split information (percentages, sample counts, or methodology) for training, validation, and testing. |
| Hardware Specification | Yes | Experiments were performed on a modern desktop computer (single thread, Intel Core i7-4790, 3.4 GHz). |
| Software Dependencies | No | The paper mentions "implemented the search in C++" and "the R-package dosearch" but does not provide specific version numbers for the C++ compiler, R, or any other software dependencies. |
| Experiment Setup | Yes | We considered DAGs with n = 7, 8, 9 nodes with 100 DAGs for each n. Edges for the DAGs were sampled randomly with average degree of 3. We sampled labels on the edges (local CSIs) with probability 0.5. Two of the nodes were considered latent and the aim was to determine whether P(Y | do(X)) can be identified. Fig. 5(a) shows the running times of Algorithm 1 with a 30 minute timeout. |