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36 results for “Causal inference”
Replication package and appendixes for Causal inference of server- and client-side code smells in web apps evolution
<p>-Analysis <br>--R scripts used to make the analisys, divided by folders<br>--Data folders used in the questions</p> <p>-Appendixes - used in the article to shwo extra tables and plots</p> <p>-data folders - Aggregation of data, each app has two files, CSV and xls</p> <p>-separated data folders - 5 files for each app, with lines corresponding to the each released official version<br>--serversmells<br>--clientsmells<br>--javascriptsmells<br>--Cloc(metrics)<br>--version (all oficial releases)</p> <p>-issues_bugs<br>--data -issues by app by release <br>--data_bugs_more - the same but only bugs, by app by release<br>--scripts - scrips used to aggregate issues (from daily issues to by release) anf the same for bugs</p> <p> </p>
Dataset Artifact for paper "Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?"
<p>Artifacts for the paper titled <strong><em>Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?</em></strong>.</p> <p>This artifact repository contains 9 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>ID</strong></td> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1</td> <td>syn_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for Causal Discovery</td> </tr> <tr> <td>2</td> <td>syn_rcd.zip</td> <td>RCD10, and RCD50 datasets for Causal Discovery</td> </tr> <tr> <td>3</td> <td>syn_causil.zip</td> <td>CausIL10, and CausIL50 datasets for Causal Discovery</td> </tr> <tr> <td>4</td> <td>rca_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for RCA</td> </tr> <tr> <td>5</td> <td>rca_rcd.zip</td> <td>RCD10, and RCD50 datasets for RCA</td> </tr> <tr> <td>6</td> <td>online-boutique.zip</td> <td>Online Boutique dataset for RCA</td> </tr> <tr> <td>7</td> <td>sock-shop-1.zip</td> <td>Sock Shop 1 dataset for RCA</td> </tr> <tr> <td>8</td> <td>sock-shop-2.zip</td> <td>Sock Shop 2 dataset for RCA</td> </tr> <tr> <td>9</td> <td>train-ticket.zip</td> <td>Train Ticket dataset for RCA</td> </tr> </tbody> </table> <p>Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system). </p> <p><strong>Details about the generation of our datasets</strong></p> <p><em>1. Synthetic datasets</em></p> <p>We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:<br><br>1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. <span>From this DAG, time series data for each node is generated using a </span><span>vector auto-regression (VAR) model. A fault is injected into a node </span><span>by altering the noise term in the VAR model for two timestamps. <br></span><span><br>2. RCD data generator [25] uses the pyAgrum package [3] to generate </span><span>a random DAG based on a given number of nodes, subsequently </span><span>generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its </span><span>conditional probability distribution.<br><br>3. CausIL data generator [15] generates causal graphs and time series data that simulate </span><span>the behavior of microservice systems. It first constructs a DAG of </span><span>services and metrics based on domain knowledge, then generates </span><span>metric data for each node of the DAG using regressors trained on </span><span>real metrics data. Unlike the CIRCA and RCD data generators, the </span><span>CausIL data generator does not have the capability to inject faults.<br><br></span>To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods. </p> <p><em>2. Data collected from benchmark microservice systems </em></p> <p>We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.</p> <p></p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/RCAEval">https://github.com/phamquiluan/RCAEval</a>.</p> <p><strong>References</strong></p> <p>As in our paper.</p>
Supplementary materials for paper "Untangling the drivers of change and policy impact in coastal wetland area in the Yangtze Estuary using causal inference"
<h1>Annual coastal wetland vegetation maps of the Yangtze Estuary from 1986 to 2021</h1> <p> </p> <h2><strong>Basic information</strong></h2> <p>Using remotely sensed data from Google Earth Engine, we generated a 30 m resolution annual dataset of Yangtze Estuary wetland vegetation for the period 1986-2021. This dataset includes three dominant vegetation types (<em>Spartina alterniflora</em>, <em>Phragmites australis</em>, and <em>Scirpus mariqueter</em>) and tidal flat areas. We combined fieldwork data and high-resolution images for accuracy assessment, achieving an overall accuracy exceeding 80% in different years. Detailed information about the mapping methods can be found in our paper and accompanying supplementary materials.</p> <h2><strong>Notes:</strong></h2> <p>In the image classification scheme: 0-Tidal flats, 1-<em>Spartina alterniflora, </em>2-<em>Phragmites australis, </em>3-<em>Scirpus mariqueter.</em></p> <h2><strong>Usage Policy:</strong></h2> <p>This dataset is a collaborative effort between East China Normal University and Deakin University. If you plan to use our data in <strong>a scientific analysis paper or other research work</strong>, we strongly recommend contacting us in advance to seek our opinions, <strong>citing the unique DOI of this dataset</strong>, and considering acknowledging our contributions or including us as co-authors.</p>
Data from: Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia
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Visual perception of shape altered by inferred causal history
<p>Dataset and stimuli relative to the following publication:</p> <p>Spröte, P., Schmidt, F., & Fleming, R. W. (2016). Visual perception of shape altered by inferred causal history. <em>Scientific Reports, 6</em>, 36245. <a href="http://dx.doi.org/10.1038/srep36245"> http://dx.doi.org/10.1038/srep36245</a></p> <p>Each folder contains the data and stimuli relative to one experiment and a text file with comments.</p> <p> </p>
Population impact of fine particulate matter on tuberculosis risk in China: A causal inference
<p>Supplementary to "Population impact of fine particulate matter on tuberculosis risk in China: A causal inference"</p>
How to select predictive models for decision making or causal inference? Experiments data
<p>This is the full result data for the experiments of the paper : Doutreligne, M., & Varoquaux, G. (2023). How to select predictive models for decision making or causal inference?, https://hal.science/hal-03946902. <br><br>The code repository is : https://github.com/soda-inria/caussim/tree/main</p> <p>The files in this dataset are the one for the most computationnally costly experiments. There is one folder for each of the four datasets used in the paper. Then, one folder for each of the experimental setup. The files required for the main figure (Fig.3) of the paper are the one labelled #fig3 in the following descriptions.</p> <p>Details on the files : </p> <p>.<br>├── acic_2016_save<br>│ ├── acic_2016__nuisance_non_linear__candidates_hist_gradient_boosting__dgp_1-77__rs_1-5<br>│ │ └── run_logs.csv: results for the experiment with non linear models for both the nuisances and the candidates<br>│ ├── acic_2016__nuisance_non_linear__candidates_ridge__dgp_1-77__rs_1-10<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates<br>│ └── acic_2016__stacked_regressor__dgp_1-77__seed_1-10<br>│ └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3<br>├── acic_2018_save<br>│ └── acic_2018__nuisance_non_linear__candidates_hist_gradient_boosting__first_uid_432<br>│ └── run_logs.csv results for the experiment with stacked models (linear and non linear) for the nuisances models and non linear models for the candidates #fig3<br>├── caussim_save<br>│ ├── caussim__linear_regressor__test_size_5000__n_datasets_1000<br>│ │ ├── run_logs.csv: results for the experiment with stacked models for the nuisances models and linear models for the candidates <br>│ │ └── simu.yaml: configuration file of the experiment<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_join_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, joined sets for the nuisances and the candidates<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_separated_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, separated sets for the nuisances and the candidates<br>│ └── caussim__stacked_regressor__test_size_5000__n_datasets_1000<br>│ ├── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and linear models for the candidates #fig3<br>│ └── simu.yaml: configuration file of the experiment<br>└── twins_save<br> └── twins__stacked_regressor__rs_1-10__overlap_0.1-3<br> └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3</p>
Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference [Datasets]
<p>This repository contains processed single-cell dataset files for DELAY.</p>
Modelling trait heterogeneity and inferring causal links in the macroevolution of growth habit in eudicot angiosperms
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Causal inference and risk prediction of gestational diabetes mellitus based on case-control study and Mendel randomization
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Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"
<p>Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS</p> <p>This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della</p>
Kinex infers causal kinases from phosphoproteomics data
<p>Reference table of 82,755 pre-scored peptides containing serine and/or threonine phosphorylation sites for 303 kinases. This reference table is used as input for Kinex, a Python package, which infers causal serine/threonine kinases from phosphoproteomics data. <br><br>Kinex is released with the GNU General Public License, openly accessible to all users at https://github.com/bedapub/kinex.</p>
The Impact of the #MeToo Movement on Language at Court - A text-based causal inference approach
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Causality Inference Datasets
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Inferring causal cell-cell communication from single-cell transcriptomics
<p>Data used to produce the analysis in "Inferring causal cell-cell communication from single-cell transcriptomics" by Almet and Nie (2022)</p>
Data from: Inferring causalities in landscape genetics: An extension of Wright's causal modeling to distance matrices
<p>Data files from Inferring causalities in landscape genetics: An extension of Wright's causal modeling to distance matrices.</p>
MRBIGR: a versatile toolbox for genetic causal inference from population-scale multi-omics data
<p>MRBIGR is a multifunctional toolkit for pre-GWAS, GWAS and post-GWAS of both traditional and multi-omics data. MRBIGR provides all the components needed to build a complete GWAS pipeline, and integrates with rich post-GWAS analysis tools such as QTL annotation and haplotype analysis. In particular, Mendelian randomization (MR) analysis, MR-based network construction, module identification and gene ontology analysis are proposed for further genetic regulation studies. Additionally, it also produces rich plots for visualization of the analysis results and other formatted data.</p> <p>This dataset is used to generate images in MRBIGR papers and can also serve as an example to demonstrate how to use MRBIGR.</p>
Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit
<p>Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined previous experience and current multisensory signals to estimate the hidden common source and subsequently updated the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how the premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.</p>
Dataset associated with Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis
<p>Full summary statistics for QTLs generated in study titled "Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis"</p> <p>The big "data" folder includes datasets for each model under subfolders Model 1A, Model 1B and Model 2 as following tree</p> <p>data<br> │ ├───Model1A<br> │ │ ├───caQTL<br> │ │ │ ├───neuron<br> │ │ │ └───progenitor<br> │ │ └───eQTL<br> │ │ ├───neuron<br> │ │ └───progenitor<br> │ ├───Model1B<br> │ └───Model2</p> <p> </p> <p> </p>
Causal Inference Research of Resistant Hypertension Treatment With Chinese Approach in a Cohort Study
ClinicalTrials.gov study NCT01904695. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.