CausalQueries1.3.1 package

Make, Update, and Query Binary Causal Models

add_dots

Helper to fill in missing do operators in causal expression

CausalQueries_internal_inherit_params

Create parameter documentation to inherit

CausalQueries

'CausalQueries'

clean_statement

Helper to clean and check the validity of causal statements specifying...

construct_commands_alter_at

make_par_values

construct_commands_other_args

make_par_values

construct_commands_param_names

make_par_values

data_helpers

Data helpers

draw_causal_type

Draw a single causal type given a parameter vector

expand_nodal_expression

Helper to expand nodal expression

get_all_data_types

Get all data types

get_estimands

helper to get estimands

get_event_probabilities

Draw event probabilities

get_parameter_matrix

Get parameter matrix

get_query_types

Look up query types

get_type_distributions

helper to get type distributions

inspection

Helpers for inspecting causal models

interpret_type

Interpret or find position in nodal type

list_non_parents

Returns a list with the nodes that are not directly pointing into a no...

make_dag

Helper to run a causal statement specifying a DAG into a data.frame ...

make_data_single

Generate full dataset

make_model

Make a model

make_par_values_stops

make_par_values_stops

make_par_values

make_par_values

make_parameters_df

function to make a parameters_df from nodal types

make_prior_distribution

Make a prior distribution from priors

observe_data

Observe data, given a strategy

parameter_setting

Setting parameters

parents_to_int

Helper to turn parents_list into a list of data_realizations column po...

perm

Produces the possible permutations of a set of nodes

plot_model

Plots a DAG in ggplot style using a causal model input

prep_stan_data

Prepare data for 'stan'

print.causal_model

Print a short summary for a causal model

print.model_query

Print a tightened summary of model queries

prior_setting

Setting priors

query_distribution

Calculate query distribution

query_helpers

Query helpers

query_model

Generate data frame for batches of causal queries

query_to_expression

Helper to turn query into a data expression

realise_outcomes

Realise outcomes

reveal_outcomes

Reveal outcomes

set_confound

Set confound

set_parameter_matrix

Set parameter matrix

set_prior_distribution

Add prior distribution draws

set_restrictions

Restrict a model

summary.causal_model

Summarizing causal models

summary.model_query

Summarizing model queries

update_model

Fit causal model using 'stan'

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>).

  • Maintainer: Till Tietz
  • License: MIT + file LICENSE
  • Last published: 2024-12-17