Function reference • CausalQueries

All functions

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_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_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_parameter_matrix(): Make parameter matrix

make_parmap(): Make parmap: a matrix mapping from parameters to data types

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

make_parameters() set_parameters() get_parameters(): 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 a 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(<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(<type_posterior>): Print a short summary for causal-type posterior distributions

print(<type_prior>): Print a short summary for causal-type prior distributions

make_priors() set_priors() get_priors(): 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_parameter_matrix(): Set parameter matrix

set_parmap(): Set parmap: a matrix mapping from parameters to data types

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>) print(<summary.causal_model>): Summarizing causal models

te(): Make treatment effect statement (positive)

update_model(): Fit causal model using 'stan'