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34 results for “Causal Model”
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<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</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits
<p>Genetic association studies have identified hundreds of independent genetic signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a relevant T2D tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million variants. We anticipate that deep learning methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.</p>
Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition
<ul> <li><strong>Name</strong>: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li><strong>Summary</strong>: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 01/06/2024</li> <li><strong>DOI</strong>: 10.5281/zenodo.11234441</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to build a set of causal diagrams of the .</li> <li><strong>Description:</strong> <br> <ul> <li><strong>Scenarios: </strong>This dataset contains the description of 20 different scenarios used in this research activity. </li> <li><strong>File 1 - individual reasons to be coded<br></strong>This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets:<br> <ul> <li><strong>Methodology</strong>: Methodology followed by the coders.</li> <li><strong>Help</strong>: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li><strong>Glossary</strong>: Glossary of terms build by the experts coding the answers. </li> <li><strong>Appliances/Flexibility/Buildings/Mobility</strong>: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li><strong>Summary</strong>: Assesment of the results.</li> </ul> </li> <li><strong>File 2 - individual microdata to sort determinants into causal threads from experts</strong>This dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li><strong>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts</strong> <p>This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears.</p> <ul> <li> <p><strong>Stage 1</strong>: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list:</p> <ul> <li> <p><strong>European panels</strong>: Group – F (8), Group–A (8). Group–FF (8), Group–M (4)</p> </li> <li> <p><strong>Latin America panels</strong>: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7)</p> </li> </ul> </li> </ul> <ul> <li> <p><strong>Stage 2</strong>: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist.</p> </li> <li> <p><strong>Stage3</strong>: In the "<em>Archetypes - Consensus Results</em>" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change).</p> </li> <li> <p><strong>Stage 4</strong>. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables:</p> <ul> <li> <p><em>Table (s)</em>: Difference and Agreements between both context: European & Latin American Archetypes. The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages.</p> </li> <li> <p><em>Table (t)</em>: This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes.</p> </li> <li> <p><em>Table (t.1)</em>: This table shows just the <strong>agreements</strong> (X) between both context: European & Latin American Archetypes.</p> </li> <li> <p><em>Table (t.2)</em>: Show the difference between both context: European (E) & Latin American Archetypes (L).</p> </li> <li> <p><em>Table (t.3)</em>: This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences.</p> </li> </ul> </li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> Data transcription from written documents and oral discussions.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned.</li> <li><strong>Ethics and legal aspects:</strong> Names of the persons involved have been removed. </li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
CausalBench A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
<p>CausalBench is a comprehensive benchmark dataset designed to evaluate the causal reasoning capabilities of large language models. The primary uses of this dataset include, but are not limited to:</p> <p>- Testing the performance of large language models on causal reasoning tasks</p> <p>- Serving as a benchmark dataset for causal reasoning research</p> <p>- Improving and developing new causal reasoning algorithms and models</p>
Data from: Forest tree breeding using genomic Markov causal models: A new approach to genomic tree breeding improvement
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SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals
<p><strong>SemEval-2020 Task 5</strong></p> <p> </p> <p><strong>Subtask-1:</strong> Recognizing Counterfactual Statements (RCS) -- Determine whether a given sentence is counterfactual or not.</p> <p><strong>Subtask-2: </strong>Detecting Antecedent and Consequent (DAC) -- Extract the antecedent and consequent part in a given counterfactual sentence.</p> <p> </p> <p>The released dataset consists of train/test data of both subtask-1 and subtask-2. In our competition, participants could only use the corresponding dataset in each subtask.</p> <p> </p> <p><strong>Task 5 Codalab Website:</strong> <a href="https://competitions.codalab.org/competitions/21691">https://competitions.codalab.org/competitions/21691</a></p>
The cultural transmission of causally (ir)relevant actions in the laboratory: Does modelling or verbal instruction lead to greater copying fidelity?
<p>This study examines the fidelity with which adults vs. children transmit a set of actions within diffusion chains (generations 1 to 3). The set consisted of causally relevant and causally irrelevant actions. Half of the adult and half of the child chains transmitted the actions via demonstration (next participant saw the previous perform the actions on video); the other half transmitted them verbally (next participant listened to audio file the previous participant describe his/her actions). We measured whether the actions were retained (i.e. re-produced by each next participant in the chain).</p>
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>
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 for: How do we measure and increase systems thinking? Comparing self-reported and performative metrics in response to building causal loop models
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Modelling trait heterogeneity and inferring causal links in the macroevolution of growth habit in eudicot angiosperms
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Data from: Development of a protocol for environmental impact studies using causal modelling
1- The global issue of water scarcity caused by climate change and human utilisation highlights the importance of an efficient assessment of water quality in freshwater systems. One of the challenges facing water management in environmental impact studies is the difficulty of inferring causality in complex systems. Traditional water assessment methods are inadequate because they are challenged to separate natural variation from the effect of human activities. 2- Knowing the causal structure of a complex ecosystem will enable managers to identify key anthropogenic, climate and flow drivers of water quality, and make informed decisions about interventions that improve water and environmental quality. In this study, I show how causal modelling can facilitate decision making for water treatment plant managers to improve their environmental management of this valuable resource. 3- Models built using causal modelling techniques, including structural equation modelling and the principles of Bayesian Networks, were utilised for management decision making purposes. The discharge load values were manipulated in the models to predict the effect of a potential intervention, e.g. treatment plant upgrade, on the values of the water quality variables in the creek. That is, water quality variables were predicted when an imaginary or counterfactual situation was imposed on the models. 4- This study showed that there would not be any observable effect of effluent on macroinvertebrate communities if the discharge loads of chlorophyll a, total organic carbon, total phosphorus, nitrate, and conductivity were reduced to 0.1 of observed values. The concentrations of environmental variables in the creek would return to their baseline levels when their corresponding discharge loads in the effluent were halved or divided by 10. 5- Based on the findings of this study, managers in the field of environmental impact studies can predict the response of a system in the presence of potential interventions under complex and uncertain conditions. The implementation of such techniques offers great promise in the wider field of environmental management where accounting for multiple factors structuring ecosystems in necessary to adequately represent causality.
Data and code for "Large language models identify causal genes in complex trait GWAS"
<p><span>This file contains the data and scripts for the preprint "Large language models identify causal genes in complex trait GWAS"</span></p>
MoCA causal model V1.0
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Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </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>
Safety and Causal Prophylactic Efficacy of KAF156 in a Controlled Human Malaria Challenge Model
ClinicalTrials.gov study NCT04072302. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Development of a protocol for environmental impact studies using causal modelling
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Causality analysis, path analyses and models between Chlorophyll a and environmental factors
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Non-linear effects of precipitation on river water quality revealed by city-level causal modelling
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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.