Package: CausalQueries 1.3.2

Till Tietz

CausalQueries: Make, Update, and Query Binary Causal Models

Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries. Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<doi:10.1017/9781316718636>) and Pearl, 2009 Causality (<doi:10.1017/CBO9780511803161>).

Authors:Clara Bicalho [ctb], Jasper Cooper [ctb], Macartan Humphreys [aut], Till Tietz [aut, cre], Alan Jacobs [aut], Merlin Heidemanns [ctb], Lily Medina [aut], Julio Solis [ctb], Georgiy Syunyaev [aut]

CausalQueries_1.3.2.tar.gz
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CausalQueries.pdf |CausalQueries.html✨
CausalQueries/json (API)
NEWS

# Install 'CausalQueries' in R:
install.packages('CausalQueries', repos = c('https://integrated-inferences.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/integrated-inferences/causalqueries/issues

Pkgdown site:https://integrated-inferences.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • democracy_data - Development and Democratization: Data for replication of analysis in *Integrated Inferences*
  • institutions_data - Institutions and growth: Data for replication of analysis in *Integrated Inferences*
  • lipids_data - Lipids: Data for Chickering and Pearl replication

On CRAN:

bayescausaldagsmixedmethodsstancpp

9.02 score 27 stars 54 scripts 693 downloads 33 exports 82 dependencies

Last updated 8 days agofrom:ecca8e4f69. Checks:1 OK, 10 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 12 2025
R-4.5-win-x86_64NOTEFeb 12 2025
R-4.5-mac-x86_64NOTEFeb 12 2025
R-4.5-mac-aarch64NOTEFeb 12 2025
R-4.5-linux-x86_64NOTEFeb 12 2025
R-4.4-win-x86_64NOTEFeb 12 2025
R-4.4-mac-x86_64NOTEFeb 12 2025
R-4.4-mac-aarch64NOTEFeb 12 2025
R-4.3-win-x86_64NOTEFeb 12 2025
R-4.3-mac-x86_64NOTEFeb 12 2025
R-4.3-mac-aarch64NOTEFeb 12 2025

Exports:collapse_datacomplementsdecreasingdraw_causal_typeexpand_dataget_all_data_typesget_event_probabilitiesget_query_typesgrabincreasinginspectinteractsinterpret_typemake_datamake_eventsmake_modelmake_parametersmake_priorsnon_decreasingnon_increasingplot_modelquery_distributionquery_modelrealise_outcomesset_confoundset_parameter_matrixset_parametersset_prior_distributionset_priorsset_restrictionssubstitutesteupdate_model

Dependencies:abindbackportsBHcachemcallrcheckmateclicolorspacecpp11descdirmultdistributionaldplyrevaluatefansifarverfastmapgenericsggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtablehighrigraphinlineisobandjsonliteknitrlabelinglatex2explatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisemgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigpolyclipposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeadersstringistringrsystemfontstensorAtibbletidygraphtidyrtidyselecttweenrutf8vctrsviridisviridisLitewithrxfunyaml

Canonical causal models

Rendered fromc-canonical-models.Rmdusingknitr::rmarkdownon Feb 12 2025.

Last update: 2025-02-12
Started: 2024-11-25

Getting Started

Rendered froma-getting-started.Rmdusingknitr::rmarkdownon Feb 12 2025.

Last update: 2025-02-12
Started: 2024-09-26

Inspecting posteriors

Rendered frome-posteriors.Rmdusingknitr::rmarkdownon Feb 12 2025.

Last update: 2025-02-12
Started: 2024-11-25

Plotting models

Rendered fromb-plotting.Rmdusingknitr::rmarkdownon Feb 12 2025.

Last update: 2024-12-17
Started: 2024-09-26

Through the front door

Rendered fromd-front-door.Rmdusingknitr::rmarkdownon Feb 12 2025.

Last update: 2025-02-12
Started: 2024-11-25

Readme and manuals

Help Manual

Help pageTopics
'CausalQueries'CausalQueries-package CausalQueries
Data helperscollapse_data data_helpers expand_data make_data make_events
Development and Democratization: Data for replication of analysis in *Integrated Inferences*democracy_data
Draw a single causal type given a parameter vectordraw_causal_type
Get all data typesget_all_data_types
Draw event probabilitiesget_event_probabilities
Look up query typesget_query_types
Helpers for inspecting causal modelsgrab inspect inspection
Institutions and growth: Data for replication of analysis in *Integrated Inferences*institutions_data
Interpret or find position in nodal typeinterpret_type
Lipids: Data for Chickering and Pearl replicationlipids_data
Make a modelmake_model
Setting parametersget_parameters make_parameters parameter_setting set_parameters
Print a short summary for a causal modelprint.causal_model
Print a tightened summary of model queriesprint.model_query
Setting priorsget_priors make_priors prior_setting set_priors
Calculate query distributionquery_distribution
Query helperscomplements decreasing increasing interacts non_decreasing non_increasing query_helpers substitutes te
Generate data frame for batches of causal queriesquery_model
Realise outcomesrealise_outcomes
Set confoundset_confound
Add prior distribution drawsset_prior_distribution
Restrict a modelset_restrictions
Summarizing causal modelsprint.summary.causal_model summary.causal_model
Summarizing model queriesprint.summary.model_query summary.model_query
Fit causal model using 'stan'update_model