Package: CausalQueries 1.4.6

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 Tietz, Medina, Syunyaev and Humphreys (2026) <doi:10.18637/jss.v117.i01>, Humphreys and Jacobs (2023) <doi:10.1017/9781316718636> and Pearl (2009) <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], Moritz Marbach [ctb]

CausalQueries_1.4.6.tar.gz
CausalQueries_1.4.6.zip(r-4.7-x86_64)CausalQueries_1.4.6.zip(r-4.7-arm64)CausalQueries_1.4.6.zip(r-4.6-x86_64)CausalQueries_1.4.6.zip(r-4.6-arm64)CausalQueries_1.4.6.zip(r-4.5-x86_64)
CausalQueries_1.4.6.tgz(r-4.6-x86_64)CausalQueries_1.4.6.tgz(r-4.6-arm64)CausalQueries_1.4.6.tgz(r-4.5-x86_64)CausalQueries_1.4.6.tgz(r-4.5-arm64)
CausalQueries_1.4.6.tar.gz(r-4.7-arm64)CausalQueries_1.4.6.tar.gz(r-4.7-x86_64)CausalQueries_1.4.6.tar.gz(r-4.6-arm64)CausalQueries_1.4.6.tar.gz(r-4.6-x86_64)
CausalQueries_1.4.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CausalQueries/json (API)

# 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/docs site:https://integrated-inferences.github.io

Uses libs:
  • onetbb– Parallelism library for C++
  • 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:

Conda:

bayescausaldagsmixedmethodsstanonetbbcpp

9.00 score 31 stars 81 scripts 1.6k downloads 33 exports 79 dependencies

Last updated from:6cdbe2f80b. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK236
linux-devel-x86_64OK296
source / vignettesOK415
linux-release-arm64OK249
linux-release-x86_64OK297
macos-release-arm64OK239
macos-release-x86_64OK808
macos-oldrel-arm64OK236
macos-oldrel-x86_64OK519
windows-devel-arm64OK465
windows-devel-x86_64OK243
windows-release-arm64OK468
windows-release-x86_64OK294
windows-oldrel-x86_64OK310
wasm-releaseOK174

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:abindbackportsbase64encBHcachemcallrcheckmateclicpp11descdirmultdistributionaldplyrevaluatefarverfastmapgenericsggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtablehighrigraphinlineisobandjsonliteknitrlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisenumDerivotelpillarpkgbuildpkgconfigpolyclipposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstanrstantoolsS7scalesStanHeadersstringistringrsystemfontstensorAtibbletidygraphtidyrtidyselecttweenrutf8vctrsviridisviridisLitewithrxfunyaml

Profiling compliers and non-compliers
References
Rendered fromf-complier-profiling.Rmdusingknitr::rmarkdown

Last update: 2025-10-26
Started: 2025-10-26

Getting Started
Make a model | Update the model | Query the model | Arbitrary queries | Output
Rendered froma-getting-started.Rmdusingknitr::rmarkdown

Last update: 2025-07-23
Started: 2024-09-26

Inspecting posteriors
Accessing the posterior | Summary of stan performance | Warnings! | Advanced diagnostics
Rendered frome-posteriors.Rmdusingknitr::rmarkdown

Last update: 2025-07-23
Started: 2024-11-25

Canonical causal models
Simple experiment | Confounded | Chain model | IV model with exclusion restriction | IV model without exclusion restriction | Mediation model with sequential ignorability | Mediation model without sequential ignorability | The napkin model | M-bias | References
Rendered fromc-canonical-models.Rmdusingknitr::rmarkdown

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

Through the front door
Try it
Rendered fromd-front-door.Rmdusingknitr::rmarkdown

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

Plotting models
A basic plot: | ggplot layers | Adding labels | Controlling positions | Controlling color | Models with unobserved confounding | More complex models | Effective node placement | Requires manual coordinates
Rendered fromb-plotting.Rmdusingknitr::rmarkdown

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

Readme and manuals

Help Manual

Help pageTopics
CausalQueries: Make, Update, and Query Binary Causal ModelsCausalQueries-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