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 [1].
Ancestral Causal Inference
Authors: Sara Magliacane, Tom Claassen, Joris M. Mooij
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it to a challenging protein data set. |
| Researcher Affiliation | Academia | Sara Magliacane VU Amsterdam & University of Amsterdam EMAIL Tom Claassen Radboud University Nijmegen EMAIL Joris M. Mooij University of Amsterdam EMAIL |
| Pseudocode | No | The paper mentions that ASP encoding is provided in the Supplementary Material but does not contain pseudocode or an algorithm block in the main text. |
| Open Source Code | Yes | Finally, we provide an open-source version of our algorithms and the evaluation framework, which can be easily extended, at http://github.com/caus-am/aci. |
| Open Datasets | Yes | Application on real data We consider the challenging task of reconstructing a signalling network from flow cytometry data [21] under different experimental conditions. [...] [21] K. Sachs, O. Perez, D. Pe er, D. Lauffenburger, and G. Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308:523 529, 2005. |
| Dataset Splits | No | The paper describes generating synthetic data and using a bootstrapping procedure for other methods, but it does not provide specific training/validation/test splits, percentages, or cross-validation details for its own model (ACI). |
| Hardware Specification | Yes | In Figure 1(a) we show the average execution times on a single core of a 2.80GHz CPU |
| Software Dependencies | Yes | For ACI we use the state-of-the-art ASP solver clingo 4 [6]. |
| Experiment Setup | Yes | For the frequentist weights we use tests based on partial correlations and Fisher s z-transform to obtain approximate p-values (see, e.g., [9]) with significance level α = 0.05. [...] For the Bayesian weights, we use the Bayesian test for conditional independence presented in [13] as implemented by HEJ with a prior probability of 0.1 for independence. [...] We perform the bootstrap by repeating the following procedure 100 times: sample randomly half of the data, perform the independence tests, run Anytime (C)FCI. [...] For 8 variables HEJ can complete only four of the first 40 simulated models before the timeout of 2500s. |