Abstracting Causal Models
Authors: Sander Beckers, Joseph Y. Halpern2678-2685
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider a sequence of successively more restrictive deļ¬nitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the right choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions. We show that procedures for combining micro-variables into macro-variables are instances of our notion of strong abstraction, as are all the examples considered by Rubenstein et al. |
| Researcher Affiliation | Academia | Sander Beckers Dept. of Philosophy and Religious Studies Utrecht University Utrecht, Netherlands srekcebrednas@gmail.com Joseph Y. Halpern Dept. of Computer Science Cornell University Ithaca, NY 14853 halpern@cs.cornell.edu |
| Pseudocode | No | The paper does not contain any sections labeled 'Pseudocode' or 'Algorithm', nor are there any structured algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the code for the methodology is available. |
| Open Datasets | No | The paper uses examples (e.g., voting scenario, object in gravitational field) to illustrate theoretical concepts, but these are not referred to as publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper does not describe experiments with datasets and therefore does not provide any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper presents theoretical work and does not involve running experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper presents theoretical work and does not involve running experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical definitions and mathematical properties of causal models. It does not describe any experiments that would require an experimental setup including hyperparameters or training settings. |