Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Ancestral Causal Inference
Authors: Sara Magliacane, Tom Claassen, Joris M. Mooij
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,262 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,034 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |