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376 results for “Causality”
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations: SW and Data
<p>This upload contains the main simulation code and related datasets used in the conference paper entitled <a href="https://ieeexplore.ieee.org/document/10437657" target="_blank" rel="nofollow noreferrer noopener">Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations</a>, which was presented at <a href="https://globecom2023.ieee-globecom.org/" target="_blank" rel="nofollow noreferrer noopener">IEEE GLOBECOM 2023</a>.</p>
The extended 'common cause': causal links between punctuated evolution and sedimentary processes
<p>The common-cause hypothesis suggests that the factors that control the availability of the Earth's sedimentary record may also affect probabilities of speciation and extinction and thus exert macroevolutionary controls on standing biodiversity. Here I show through computational modeling that common causes may also link sedimentary biases and microevolutionary processes of trait evolution. Using Gould's classic "evolutionary microcosm" of Bermuda and its diverse endemic clade of land snails, Poecilozonites, I show that the glacial-interglacial sea level cycles that toggle local sedimentation between rapid eolian accumulation and slow pedogenesis could easily toggle trait evolution between rapid bursts of morphological change driven small effective population size, disruptions in gene flow, and "genetic surfing" expansion events punctuated with long periods of slow morphological evolution associated with geographic range coalescence, large effective population size, and panmixia. The pattern produced by this interaction is expected to be similar to that produced by punctuated equilibria, even without accompanying speciation events. The spatial dynamics of this system are expected to produce patterns of random trait evolution that are more likely multi-rate evolutionary models than like the standard single-rate Brownian motion models that are currently used as the null model in many phylogenetic comparative methods. The Bermudian example of links between sedimentation and evolution is arguably extreme, but the principles of the extended common cause are likely to extend to many other paleontological systems.</p>
The causal role of the somatosensory cortex in prosocial behavior - Pain Localizer
<p>Participants with no reported neurological, psychiatric, or other medical problems or any contraindication to fMRI, underwent a total of 40 electrical and 40 mechanical stimulations, split in 8 runs (4 electrical and 4 mechanical) of 10 (5 high intensity and 5 low intensity) stimulations each on their right hand. For more information about task please refer to the pubblication. See the associated readme file for more details.</p> <p> </p>
The causal role of the somatosensory cortex in prosocial behavior - EEG dataset
<p>Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt</p>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p> <div> </div>
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>
Data - Attack of the clones: population genetics reveals clonality of Colletotrichum lupini, the causal agent of lupin anthracnose
<p><em>Colletotrichum lupini</em>, causing lupin anthracnose, is one of the worst pathogens to lupin cultivation worldwide. Understanding its population structure and evolutionary potential is crucial to design successful disease management strategies. The objective of this study was to employ population genetics to investigate the genetic diversity, evolutionary dynamics and molecular basis of host-speciation of this notorious lupin pathogen. A collection of globally representative <em>C. lupini </em>isolates was genotyped through triple digest restriction-site associated DNA sequencing (3D-RADseq), resulting in a dataset of unparalleled resolution. Phylogenetic and structural analysis could distinguish four (I – IV) independent lineages. The strong population structure, low recombination rate and slow linkage decay strongly indicate that <em>C. lupini</em> reproduces clonally. Different morphologies and virulence patterns on white and Andean lupin were observed between and within clonal lineages. Lineage II isolates were shown to have a mini chromosome which was also partly present in lineage III and IV, but not in lineage I isolates. Variation in the presence of this mini-chromosome could indicate a function related to virulence or host-speciation. All four lineages were present in the South American Andes region, which is concluded to be the center of origin of this species. Only members of lineage II have been found outside South America since the 1990s, indicating it as the current pandemic population. As a seed-borne pathogen, <em>C. lupini</em> has mainly spread through infected but symptomless seeds, stressing the importance of phytosanitary measures to prevent future outbreaks of strains that are yet confined to South America.</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>
Causal Analysis of Google Code Jam Contest Data
<p>This archive is the replication package for the paper:</p> <p>Carlo A. Furia, Richard Torkar, Robert Feldt: <em>Towards Causal Analysis of Empirical Software Engineering Data — The Impact of Programming Languages on Coding Competitions</em>. <a href="https://arxiv.org/abs/2301.07524">arXiv:2301.07524</a>. January 2023.</p>
Data for: Causal mechanisms for negative impacts of energy development inform management triggers for sagebrush birds
<p>Estimated population trends can identify declining species to focus biological conservation, but monitoring may fail to illuminate causes of population change and strategies for reversing declines. Monitoring programs can relate trends with environmental attributes to test causal hypotheses, but typical analytical approaches do not explicitly support causal inference, diluting available data for informing conservation. The U.S. Bureau of Land Management (BLM) extended Integrated Monitoring in Bird Conservation Regions with a quasi-experimental sampling design over a 10-year period (2010–2019) to evaluate impacts of oil and gas development on sagebrush birds within the Atlantic Rim Natural Gas Development Project in southern Wyoming. We analyzed resulting data using a multi-scale community occupancy model to estimate trends in species occupancy and richness relevant to management triggers. Additionally, we employed path analysis to evaluate mechanisms underlying observed trends to inform potential management responses. Fine-scale occupancy for sage thrasher (<em>Oreoscoptes montanus</em>) declined within the high-development stratum at a rate sufficient to meet an<em> a priori</em> management trigger established by the BLM. Two additional sagebrush-associated species, Brewer's (<em>Spizella breweri</em>) and sagebrush sparrow (<em>Artemisiospiza nevadensis</em>), exhibited negative development relationships with trend, as did overall species richness, and richness of grassland, sagebrush, and generalist guilds. We identified well pad density and invasive plants associated with energy development as causal factors contributing to these negative development impacts. We demonstrate an analytical approach for both estimating occupancy trends and identifying underlying causes to inform conservation action. Reducing the development footprint, including well pad density and associated invasive plants, could help reduce or limit impacts on birds within this landscape.</p>
Data and code for "Representing storylines with causal networks to support decision making: framework and example"
<p>Data and code for the paper "Representing storylines with causal networks to support decision making: framework and example", along with the Shiny webapp code accompanying the paper. The paper has been submitted to the journal of Climate Risk Management and is currently under review.</p>
Causal identification of single-cell experimental perturbation effects with CINEMA-OT
<p>Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus and cigarette smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.</p>
Touché23-Evidence-Retrieval-for-Causal-Questions
<p>Data for the <a href="https://touche.webis.de/clef23/touche23-web/evidence-retrieval-for-causal-questions.html">Evidence Retrieval for Causal Questions</a> task at Touché 2023.</p>
"CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data" datasets
<p>Datasets from the article:</p> <p><strong>CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data </strong></p> <p>by Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, Nikita Lagrange, Arianna Mencattini, Maria Carla Parrini, Eugenio Martinelli, Hervé Isambert.</p> <p> </p> <p>The <strong>original videos</strong> are uploaded as: "20161230.zip", "20170105.rar", "Video_2017_0517.zip".</p> <p><strong>Details </strong>for each <strong>experiment </strong>can be found in: "Experiments' details.zip".</p> <p>The <strong>ROIs </strong>(ROI: Region of Interest) are uploaded as .tif files in: "EXTRACTED ROIs.zip".</p> <p>The <strong>MATLAB data</strong> is uploaded in "MATLAB_DATA.rar" and includes: the cancer cells' trajectories (subfolder: "TUMOR TRAJECTORIES"), the immune cells' trajectories (subfolder: "IMMUNE TRAJECTORIES"), the ROIs videos as .mat files for the detection of cells (subfolder: "ROI MAT"), the ROIs further cropped for the extraction of shape descriptors (folder: "ROI_TU MAT"). The ROIs videos .mat files included in the last two subfolders are stopped after their apoptosis has been detected.</p>
Causal Relationships Between LC-omega-3-enriched Diet and Cognition
ClinicalTrials.gov study NCT01625195. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Causal Role of Top-Down Theta Oscillations in Prioritization
ClinicalTrials.gov study NCT06252532. IPD Sharing: YES. Countries: 1. Publications: 2.
Exploratory Study of Danicamtiv in Patients With Primary Dilated Cardiomyopathy (DCM) Due to Genetic Variants or Other Causalities
ClinicalTrials.gov study NCT04572893. IPD Sharing: Not stated. Countries: 4. Publications: 1.
Data from: A causal role of anterior prefrontal-putamen circuit for response inhibition revealed by transcranial ultrasound stimulation in humans
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Causal identification of single-cell experimental perturbation effects with CINEMA-OT
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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.