Causal analysis of Covid-19 Spread in Germany
Authors: Atalanti Mastakouri, Bernhard Schölkopf
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states. We loose a strictly formulated assumption for a causal feature selection method for time series data, robust to latent confounders, which we subsequently apply on Covid-19 case numbers. We present findings about the spread of the virus in Germany and the causal impact of restriction measures, discussing the role of various policies in containing the spread. Since our results are based on rather limited target time series (only the numbers of reported cases), care should be exercised in interpreting them. However, it is encouraging that already such limited data seems to contain causal signals. |
| Researcher Affiliation | Academia | Atalanti A. Mastakouri Department of Empirical Inference Max Planck Institute for Intelligent Systems Tübingen, Germany atalanti.mastakouri@tuebingen.mpg.de Bernhard Schölkopf Department of Empirical Inference Max Planck Institute for Intelligent Systems Tübingen, Germany bs@tuebingen.mpg.de |
| Pseudocode | No | The paper describes the Sy PI method conceptually but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'We provide the data in the supplement and here https://owncloud.tuebingen.mpg.de/index.php/s/r4dPdpSBAzP6Ee5.', which is a link to the data, not the source code for the methodology. |
| Open Datasets | Yes | The data are taken from the official reports of the Robert-Koch Institute, last downloaded on 15/05/2020 [20]. ... [20] RKI. Covid-19 reported cases in german federal and sistrict regions, 2020. https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/data. |
| Dataset Splits | No | The paper does not explicitly mention specific training, validation, or test dataset splits or percentages for reproducing the experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using the Sy PI method and comparing it to Lasso-Granger and ts FCI, but it does not specify any software dependencies with version numbers (e.g., Python, specific libraries). |
| Experiment Setup | Yes | Sy PI operates with two thresholds for those two tests: one for rejecting conditional independencies (condition 1), and another for accepting conditional independencies (condition 2). ... We thus examined values of threshold-1 in {0.01, 0.05} and values for threshold-2 in {0.1, 0.2}. We report the causal findings for the looser combination (0.05, 0.1) in Fig. 3a and for all four in the App. Fig. 5. ... The default thresholds of Sy PI (0.01, 0.2) were used. |