Identification for Tree-Shaped Structural Causal Models in Polynomial Time
Authors: Aaryan Gupta, Markus Bläser
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we present a randomized polynomial-time algorithm, which solves the identification problem for tree-shaped SCMs. For every structural parameter, our algorithms decides whether it is generically identifiable, generically 2-identifiable, or generically unidentifiable. (No other cases can occur.) In the first two cases, it provides one or two fractional affine square root terms of polynomials (FASTPs) for the corresponding parameter, respectively. In particular, our algorithm is not only polynomial time, but also complete for for tree-shaped SCMs. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology Bombay, Mumbai, India 2Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany aaryanguptaxyz@gmail.com, mblaeser@cs.uni-saarland.de |
| Pseudocode | Yes | Algorithm 1: Finding identifying cycles; Algorithm 2: Identification |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology. It mentions a "full version (Gupta and Bl aser 2023)" available on CoRR, which is a preprint server, not a code repository. |
| Open Datasets | No | The paper focuses on theoretical contributions (algorithm design and proofs for structural causal models) and does not involve empirical training on datasets. Therefore, no information about public datasets or their access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. Thus, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes theoretical algorithm design and analysis. It does not mention any hardware specifications (e.g., GPU/CPU models, memory) used for running experiments, as no empirical experiments are detailed. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and mathematical proofs. It refers to mathematical concepts and tools like Gr obner bases, but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers) required for experimental reproduction. |
| Experiment Setup | No | The paper describes theoretical algorithm design and analysis, not empirical experiments. Therefore, it does not include details such as hyperparameters, training configurations, or other specific experimental setup parameters. |