Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
Authors: Bryant Chen, Daniel Kumor, Elias Bareinboim
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We developed a novel approach to identification and model testing in linear structural equation models (SEMs) based on auxiliary variables (AVs), which generalizes a widely-used family of methods known as instrumental variables. The identification problem is concerned with the conditions under which causal parameters can be uniquely estimated from an observational, noncausal covariance matrix. In this paper, we provide an algorithm for the identification of causal parameters in linear structural models that subsumes previous state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1IBM Research, San Jose, California, USA 2Purdue University, West Lafeyette, Indiana, USA. |
| Pseudocode | Yes | Algorithm 1 q ID(G, Σ, IDEdges) and Algorithm 2 Finds overidentifying constraints for G. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs, and therefore does not involve training on datasets or provide information about dataset availability. |
| Dataset Splits | No | As this is a theoretical paper presenting algorithms and proofs, there are no dataset splits mentioned for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameters or training configurations. |