Minimum Intervention Cover of a Causal Graph
Authors: Saravanan Kandasamy, Arnab Bhattacharyya, Vasant G. Honavar2876-2885
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide an algorithm that, given a causal graph G, determines MIC(G), a minimum intervention cover of G, i.e., a minimum set of interventions that suffices for identifying every causal effect that is identifiable in a causal model characterized by G. We establish the completeness of do-calculus for computing MIC(G). MIC(G) effectively offers an efficient compilation of all of the information obtainable from all possible interventions in a causal model characterized by G. Minimum intervention cover finds applications in a variety of contexts including counterfactual inference, and generalizing causal effects across experimental settings. We analyze the computational complexity of minimum intervention cover and identify some special cases of practical interest in which MIC(G) can be computed in time that is polynomial in the size of G. |
| Researcher Affiliation | Academia | Saravanan Kandasamy Tata Institute of Fundamental Research Mumbai, India Arnab Bhattacharyya Dept. of Computer Science National Univ. of Singapore Singapore & Dept. of Computer Science & Automation Indian Institute of Science Bangalore, India Vasant G Honavar Artificial Intelligence Research Laboratory College of Information Sciences & Technology, Pennsylvania State Univ. University Park, PA, USA |
| Pseudocode | Yes | Algorithm 1 MIC(G) Algorithm 2 FINDALLBUSHES(G) Algorithm 3 FINDPAIRS(A , B \ {W}, G) Algorithm 4 VERTEXCONSTRUCTION(IB,G) |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the methodology is being released. |
| Open Datasets | No | The paper is theoretical and does not involve training on datasets; therefore, it does not mention public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets; therefore, it does not specify train/test/validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental work requiring specific hardware; therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs, not experimental implementation, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |