Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Authors: Daniel Kumor, Bryant Chen, Elias Bareinboim
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate graphical conditions to allow efficient identification in arbitrary linear structural causal models (SCMs). In particular, we develop a method to efficiently find unconditioned instrumental subsets... Further, we prove that determining whether an effect can be identified with TSID (Weihs et al., 2017), a method more powerful than unconditioned instrumental sets and other efficient identification algorithms, is NP-Complete. Finally, building on the idea of flow constraints, we introduce a new and efficient criterion called Instrumental Cutsets (IC), which is able to solve for parameters missed by all other existing polynomial-time algorithms. |
| Researcher Affiliation | Collaboration | Daniel Kumor Purdue University dkumor@purdue.edu Bryant Chen Brex Inc. bryant@brex.com Elias Bareinboim Columbia University eb@cs.columbia.edu |
| Pseudocode | Yes | Algorithm 1 Find Maximal Match-Block given DAG G, source nodes S and target nodes T; Algorithm 2 IC solves for edges incoming to y given a set of known edges Λ |
| Open Source Code | Yes | A Python implementation is available at https://github.com/dkumor/instrumental-cutsets |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving public datasets or data access information. |
| Dataset Splits | No | The paper is theoretical and does not mention dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify hardware used for experiments. |
| Software Dependencies | No | The paper mentions a 'Python implementation' but does not provide specific version numbers for Python or any required libraries/solvers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |