Combining Experts’ Causal Judgments
Authors: Dalal Alrajeh, Hana Chockler, Joseph Halpern
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formally define the notion of an effective intervention, and then consider how experts causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible, and show how compatible causal models can be combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples. |
| Researcher Affiliation | Academia | Dalal Alrajeh Department of Computing Imperial College London dalal.alrajeh04@imperial.ac.uk Hana Chockler Department of Informatics King s College London Hana.Chockler@kcl.ac.uk Joseph Y. Halpern Computer Science Department Cornell University halpern@cs.cornell.edu |
| Pseudocode | No | The paper describes its definitions and framework using mathematical notation and prose, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any concrete statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper uses illustrative 'real-life examples' but does not conduct empirical experiments, hence it does not specify any dataset used for training or provide access information for one. |
| Dataset Splits | No | The paper is theoretical and illustrates its concepts with examples rather than conducting empirical experiments with data splits. Therefore, it does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments, thus it does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on formal definitions and proofs, not empirical implementations. It does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup, thus it does not include details such as hyperparameters or training configurations. |