Causal Identification with Matrix Equations
Authors: Sanghack Lee, Elias Bareinboim
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we develop a new causal identification algorithm which utilizes both graphical criteria and matrix equations. Specifically, we first characterize the relationships between certain graphically-driven formulae and matrix multiplications. With such characterizations, we broaden the spectrum of proxy variable based identification conditions and further propose novel intermediary criteria based on the pseudoinverse of a matrix. Finally, we devise a causal effect identification algorithm, which accepts as input a collection of marginal, conditional, and interventional distributions, integrating enriched matrix-based criteria into a graphical identification approach. We develop a general identification algorithm that amalgamates graphical and matrical approaches, returning an identification formula for a causal query given a causal diagram and a set of marginal, experimental, and conditional distributions. We show that this method subsumes current state of the art in the literature. Theorem 4. ID-ME is sound. Theorem 5. ID-ME strictly subsumes proxy criteria [8, 15], GID(-PO) [14, 12], m ID, or e ID [11]. |
| Researcher Affiliation | Academia | Sanghack Lee Graduate School of Data Science Seoul National University Seoul, South Korea sanghack@snu.ac.kr Elias Bareinboim Department of Computer Science Columbia University New York, USA eb@cs.columbia.edu |
| Pseudocode | Yes | We present a causal identification algorithm ID-ME (Alg. 1) and provides "Algorithm 1 ID-ME". |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. It only refers to a technical report for omitted proofs and derivations. |
| Open Datasets | No | The paper is theoretical and does not involve the use of any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets or their partitioning into train/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training settings. |