Make, Update, and Query Binary Causal Models

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Documentation for package ‘CausalQueries’ version 1.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

CausalQueries-package	'CausalQueries'
collapse_data	Make compact data with data strategies
complements	Make statement for complements
data_type_names	Data type names
decreasing	Make monotonicity statement (negative)
democracy_data	Development and Democratization: Data for replication of analysis in *Integrated Inferences*
draw_causal_type	Draw a single causal type given a parameter vector
expand_data	Expand compact data object to data frame
expand_wildcard	Expand wildcard
find_rounding_threshold	helper to find rounding thresholds for print methods
get_all_data_types	Get all data types
get_ambiguities_matrix	Get ambiguities matrix
get_event_probabilities	Draw event probabilities
get_parameters	Setting parameters
get_parameter_names	Get parameter names
get_parents	Get list of parents of all nodes in a model
get_parmap	Get parmap: a matrix mapping from parameters to data types
get_priors	Setting priors
get_query_types	Look up query types
get_type_prob	Get type probabilities
get_type_prob_c	generates one draw from type probability distribution for each type in P
get_type_prob_multiple_c	generates n draws from type probability distribution for each type in P
grab	Grab
increasing	Make monotonicity statement (positive)
institutions_data	Institutions and growth: Data for replication of analysis in *Integrated Inferences*
interacts	Make statement for any interaction
interpret_type	Interpret or find position in nodal type
lipids_data	Lipids: Data for Chickering and Pearl replication
make_data	Make data
make_events	Make data in compact form
make_model	Make a model
make_parameters	Setting parameters
make_parameter_matrix	Make parameter matrix
make_parmap	Make parmap: a matrix mapping from parameters to data types
make_priors	Setting priors
make_prior_distribution	Make a prior distribution from priors
non_decreasing	Make monotonicity statement (non negative)
non_increasing	Make monotonicity statement (non positive)
observe_data	Observe data, given a strategy
parameter_setting	Setting parameters
print.causal_model	Print a short summary for a causal model
print.causal_types	Print a short summary for causal_model causal-types
print.dag	Print a short summary for a causal_model DAG
print.event_probabilities	Print a short summary for event probabilities
print.model_query	Print a tightened summary of model queries
print.nodal_types	Print a short summary for causal_model nodal-types
print.nodes	Print a short summary for causal_model nodes
print.parameters	Print a short summary for causal_model parameters
print.parameters_df	Print a short summary for a causal_model parameters data-frame
print.parameters_posterior	Print a short summary for causal_model parameter posterior distributions
print.parameters_prior	Print a short summary for causal_model parameter prior distributions
print.parameter_mapping	Print a short summary for paramater mapping matrix
print.parents_df	Print a short summary for a causal_model parents data-frame
print.posterior_event_probabilities	Print a short summary of posterior_event_probabilities
print.stan_summary	Print a short summary for stan fit
print.statement	Print a short summary for a causal_model statement
print.summary.causal_model	Summarizing causal models
print.type_distribution	Print a short summary for causal-type posterior distributions
print.type_prior	Print a short summary for causal-type prior distributions
prior_setting	Setting priors
query_distribution	Calculate query distribution
query_model	Generate estimands dataframe
realise_outcomes	Realise outcomes
set_ambiguities_matrix	Set ambiguity matrix
set_confound	Set confound
set_parameters	Setting parameters
set_parameter_matrix	Set parameter matrix
set_parmap	Set parmap: a matrix mapping from parameters to data types
set_priors	Setting priors
set_prior_distribution	Add prior distribution draws
set_restrictions	Restrict a model
simulate_data	simulate_data is an alias for make_data
substitutes	Make statement for substitutes
summarise_distribution	helper to compute mean and sd of a distribution data.frame
summary.causal_model	Summarizing causal models
te	Make treatment effect statement (positive)
update_model	Fit causal model using 'stan'