Identification and Overidentification of Linear Structural Equation Models
Authors: Bryant Chen
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we address the problems of identifying linear structural equation models and discovering the constraints they imply. We first extend the half-trek criterion to cover a broader class of models and apply our extension to finding testable constraints implied by the model. We then show that any semi-Markovian linear model can be recursively decomposed into simpler sub-models, resulting in improved identification and constraint discovery power. Finally, we show that, unlike the existing methods developed for linear models, the resulting method subsumes the identification and constraint discovery algorithms for non-parametric models. |
| Researcher Affiliation | Academia | Bryant Chen University of California, Los Angeles Computer Science Department Los Angeles, CA, 90095-1596, USA |
| Pseudocode | Yes | An algorithm that utilizes the g-HTC and Theorem 1 to identify as many coefficients as possible in recursive or non-recursive linear SEMs is given in the Appendix. ... An algorithm that identifies coefficients and finds HT-constraints for a recursive or non-recursive linear SEM is given in the Appendix. ... (see also Algorithm 3 in the Appendix, which utilizes recursive decomposition to identify coefficients and output HT-constraints) ... Algorithm 2 (see Appendix) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments run on a dataset, therefore no public dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments run on a dataset, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |