Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

Authors: Benjie Wang, Clare Lyle, Marta Kwiatkowska

IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 benjie.wang@cs.ox.ac.uk
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.