Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
Authors: Benjie Wang, Clare Lyle, Marta Kwiatkowska
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the methods yield useful and interpretable bounds for a range of practical networks, paving the way towards provably causally robust decision-making systems.The results are shown in Table 1.In Table 2 we show the performance of our joint compilation approach on a number of benchmark Bayesian networks...In Table 3 we analyse the quality of our upper and lower bounds on interventional robustness. |
| Researcher Affiliation | Academia | Benjie Wang , Clare Lyle and Marta Kwiatkowska University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1: UB(AC, e, W ) (Upper Bounding) and Algorithm 2: Lower Bounding |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | In Table 2 we show the performance of our joint compilation approach on a number of benchmark Bayesian networks...The 'insurance' network refers to [Binder et al., 1997], which is a proper citation for a public dataset. 'win95pts' and 'hepar2' are also commonly used benchmarks in this field. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'C2D compiler [Darwiche, 2004]' but does not provide a specific version number for this or any other software component used to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |