Package index • causaloptim

Shiny interface

Most users will only need to use this function.

specify_graph(): Shiny interface to specify network structure and compute bounds

causaloptim-package causaloptim: An Interface to Specify Causal Graphs and Compute Bounds on Causal Effects

Direct interface

Functions for creating and interacting with the algorithm.

analyze_graph(): Analyze the causal graph and effect to determine constraints and objective

optimize_effect_2(): Run the optimizer to obtain symbolic bounds

initialize_graph(): Initialize an igraph object for use with causaloptim

create_causalmodel(): Create a structural causal model from a graph or a set of response functions

create_response_function(): Translate regular DAG to response functions

create_linearcausalproblem(): Create linear causal problem from causal model and effect

latex_bounds(): Latex bounds equations

sample_distribution(): Sample a distribution of observable probabilities that satisfy the causal model

rdirichlet(): Sample from a Dirichlet distribution

check_constraints_violated(): Check whether any of the observable constraints implied by the causal model are violated for a given distribution of observables

check_linear_objective(): Check linearity of objective function implied by a causal model and effect

plot(<linearcausalproblem>): Plot the graph from the causal problem with a legend describing attributes

print(<linearcausalproblem>): Print the causal problem

print(<causalmodel>): Print relevant information about the causal model

Utilities

Internal functions and deprecated

causalproblemcheck(): Check conditions on causal problem

constraintscheck(): Check constraints

create_effect_vector(): Translate target effect to vector of response variables

create_q_matrix(): Translate response functions into matrix of counterfactuals

create_response_function(): Translate regular DAG to response functions

graphrescheck(): Check conditions on digraph

get_default_effect(): Define default effect for a given graph

opt_effect(): Compute a bound on the average causal effect

parse_constraints(): Parse text that defines a the constraints

parse_effect(): Parse text that defines a causal effect

plot_graphres(): Plot the analyzed graph object

querycheck(): Check conditions on query

simulate_bounds(): Simulate bounds

btm_var(): Recursive function to get the last name in a list

check_parents(): Check for paths from from to to

find_all_paths(): Find all paths in a causal model

interpret_bounds(): Convert bounds string to a function

list_to_path(): Recursive function to translate an effect list to a path sequence

update_effect(): Update the effect in a linearcausalproblem object