Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Authors: Daniel Kumor, Carlos Cinelli, Elias Bareinboim
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a polynomial-time algorithm for identification of structural coefficients in linear causal models that subsumes previous efficient state-of-the-art methods, unifying several disparate approaches to identification in this setting. Building on these results, we develop a procedure for identifying total causal effects in linear systems. |
| Researcher Affiliation | Academia | 1Dept. of Computer Science, Purdue University, West Lafayette, IN, USA 2Dept. of Statistics, University of California, Los Angeles, CA, USA 3Dept. of Computer Science, Columbia University, New York, NY, USA. |
| Pseudocode | Yes | Algorithm 1 AC: is given a graph, target vertex x, a set of candidate instruments (which can themselves be AVs), a set of identified structural parameters, and returns the Auxiliary Cutset for x Algorithm 2 ACID: Given a graph, returns a set of identifiable structural parameters. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or their setup details, such as hyperparameters or training configurations. |