riantr/moonbit_dowhy/graph

README
- ::causal_graphCausalModel::causal_graph
- ::dataCausalModel::data
- ::estimate_effectCausalModel::estimate_effect
- ::identified_estimandCausalModel::identified_estimand
- ::identify_effectCausalModel::identify_effect
- ::newCausalModel::new
- ::refute_estimateCausalModel::refute_estimate

riantr/moonbit_dowhy/graph does not have a README file

CausalGraph

pub struct CausalGraph {
  treatment_name : Array[String]
  outcome_name : Array[String]
  graph : DirectedGraph
  observed : Map[String, Bool]
  effect_modifiers : Array[String]
}

A causal graph with the semantic role of every node.

CausalGraph::add_missing_nodes_as_common_causes

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fn CausalGraph::add_missing_nodes_as_common_causes(self : CausalGraph, observed_node_names : Array[String]) -> DirectedGraph

Add the columns of the data that are not part of the graph as confounders.

CausalGraph::add_unobserved_common_cause

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fn CausalGraph::add_unobserved_common_cause(self : CausalGraph) -> DirectedGraph

Add an unobserved common cause to the graph.

CausalGraph::all_observed

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fn CausalGraph::all_observed(self : CausalGraph, node_names : Array[String]) -> Bool

Whether every given node is observed.

CausalGraph::check_dseparation

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fn CausalGraph::check_dseparation(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String], nodes3 : Array[String], new_graph : DirectedGraph?) -> Bool

Whether the two node sets are d-separated given the conditioning set.

CausalGraph::check_valid_backdoor_set

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fn CausalGraph::check_valid_backdoor_set(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String], nodes3 : Array[String], new_graph : DirectedGraph?) -> Bool

Whether the candidate set blocks the backdoor paths.

CausalGraph::check_valid_frontdoor_set

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fn CausalGraph::check_valid_frontdoor_set(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String], candidate_nodes : Array[String], frontdoor_paths : Array[Array[String]], new_graph : DirectedGraph?) -> Bool

Whether the candidate set is a valid frontdoor set.

CausalGraph::check_valid_mediation_set

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fn CausalGraph::check_valid_mediation_set(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String], candidate_nodes : Array[String], mediation_paths : Array[Array[String]]) -> Bool

Whether the candidate set mediates the effect.

CausalGraph::directed_graph

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fn CausalGraph::directed_graph(self : CausalGraph) -> DirectedGraph

The underlying directed graph.

CausalGraph::do_surgery

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fn CausalGraph::do_surgery(self : CausalGraph, node_names : Array[String], remove_outgoing_edges : Bool, remove_incoming_edges : Bool, target_node_names : Array[String], remove_only_direct_edges_to_target : Bool) -> DirectedGraph

A graph with the edges of the given nodes removed.

CausalGraph::get_adjacency_matrix

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fn CausalGraph::get_adjacency_matrix(self : CausalGraph) -> @riantr/moonbit_dowhy/utils.Matrix
Matrix

The adjacency matrix of the graph.

CausalGraph::get_all_directed_paths

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fn CausalGraph::get_all_directed_paths(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String]) -> Array[Array[String]]

The directed paths between two singleton node sets.

CausalGraph::get_all_nodes

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fn CausalGraph::get_all_nodes(self : CausalGraph, include_unobserved : Bool) -> Array[String]

The nodes of the graph.

CausalGraph::get_ancestors

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fn CausalGraph::get_ancestors(self : CausalGraph, node_name : String) -> @moonbitlang/core/set.Set
Set[String]

The strict ancestors of a node.

CausalGraph::get_common_causes

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fn CausalGraph::get_common_causes(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String]) -> Array[String]

The common causes of two node sets.

The treatment contributes its ancestors, the outcome contributes its parents and their ancestors.

CausalGraph::get_descendants

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fn CausalGraph::get_descendants(self : CausalGraph, nodes : Array[String]) -> @moonbitlang/core/set.Set
Set[String]

The strict descendants of a set of nodes.

CausalGraph::get_effect_modifiers

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fn CausalGraph::get_effect_modifiers(self : CausalGraph, nodes1 : Array[String], nodes2 : Array[String]) -> Array[String]

The effect modifiers of the graph.

CausalGraph::get_instruments

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fn CausalGraph::get_instruments(self : CausalGraph, treatment_nodes : Array[String], outcome_nodes : Array[String]) -> Array[String]

The instruments of the treatment.

CausalGraph::get_parents

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fn CausalGraph::get_parents(self : CausalGraph, node_name : String) -> Array[String]

The parents of a node.

CausalGraph::has_directed_path

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fn CausalGraph::has_directed_path(self : CausalGraph, action_nodes : Array[String], outcome_nodes : Array[String]) -> Bool

Whether every treatment reaches an outcome and vice versa.

CausalGraph::is_observed

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fn CausalGraph::is_observed(self : CausalGraph, node_name : String) -> Bool

Whether a node is observed.

CausalGraph::new

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fn CausalGraph::new(treatment_name : Array[String], outcome_name : Array[String], graph : DirectedGraph?, common_cause_names : Array[String], instrument_names : Array[String], effect_modifier_names : Array[String], mediator_names : Array[String], observed_node_names : Array[String]) -> CausalGraph

Build a causal graph.

When graph is given it is used as is, otherwise the graph is built from the semantic roles of the variables.

CausalGraph::observed_nodes

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fn CausalGraph::observed_nodes(self : CausalGraph) -> Array[String]

The observed nodes of the graph.

CausalGraph::outcomes

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fn CausalGraph::outcomes(self : CausalGraph) -> Array[String]

The outcome variables.

CausalGraph::treatments

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fn CausalGraph::treatments(self : CausalGraph) -> Array[String]

The treatment variables.

DirectedGraph

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pub struct DirectedGraph {
  nodes : Array[String]
  node_set : @moonbitlang/core/set.Set
Set[String]
  succ : Map[String, Array[String]]
  pred : Map[String, Array[String]]
  edges : Array[(String, String)]
}

A directed graph, the substrate of every causal graph in the library.

Node and edge insertion order is preserved, which keeps the generated results (adjustment sets, paths, topological orders) deterministic.

DirectedGraph::add_edge

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fn DirectedGraph::add_edge(self : DirectedGraph, from : String, to : String) -> Unit

Add an edge, creating the endpoints when they are new.

DirectedGraph::add_node

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fn DirectedGraph::add_node(self : DirectedGraph, name : String) -> Unit

Add a node, ignoring duplicates.

DirectedGraph::all_simple_paths

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fn DirectedGraph::all_simple_paths(self : DirectedGraph, from : String, to : String) -> Array[Array[String]]

All simple paths from from to to.

The paths are enumerated in the order the neighbours are stored, which keeps the result deterministic.

DirectedGraph::all_simple_paths_undirected

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fn DirectedGraph::all_simple_paths_undirected(self : DirectedGraph, from : String, to : String) -> Array[Array[String]]

All simple paths between two nodes, ignoring the direction of the edges.

The backdoor paths live in the underlying undirected graph, so the traversal walks both the parents and the children of a node.

DirectedGraph::ancestors

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fn DirectedGraph::ancestors(self : DirectedGraph, name : String) -> @moonbitlang/core/set.Set
Set[String]

The strict ancestors of a node.

DirectedGraph::ancestors_of

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fn DirectedGraph::ancestors_of(self : DirectedGraph, names : Array[String]) -> @moonbitlang/core/set.Set
Set[String]

The union of the ancestors of several nodes.

DirectedGraph::copy

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fn DirectedGraph::copy(self : DirectedGraph) -> DirectedGraph

A copy of the graph.

DirectedGraph::descendants

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fn DirectedGraph::descendants(self : DirectedGraph, name : String) -> @moonbitlang/core/set.Set
Set[String]

The strict descendants of a node.

DirectedGraph::descendants_of

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fn DirectedGraph::descendants_of(self : DirectedGraph, names : Array[String]) -> @moonbitlang/core/set.Set
Set[String]

The union of the descendants of several nodes.

DirectedGraph::edge_count

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fn DirectedGraph::edge_count(self : DirectedGraph) -> Int

The number of edges.

DirectedGraph::edge_list

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fn DirectedGraph::edge_list(self : DirectedGraph) -> Array[(String, String)]

The edges of the graph in insertion order.

DirectedGraph::has_edge

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fn DirectedGraph::has_edge(self : DirectedGraph, from : String, to : String) -> Bool

Whether the graph contains the edge.

DirectedGraph::has_node

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fn DirectedGraph::has_node(self : DirectedGraph, name : String) -> Bool

Whether the graph contains the node.

DirectedGraph::has_path

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fn DirectedGraph::has_path(self : DirectedGraph, from : String, to : String) -> Bool

Whether a path exists from from to to.

DirectedGraph::is_dag

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fn DirectedGraph::is_dag(self : DirectedGraph) -> Bool

Whether the graph is acyclic.

DirectedGraph::new

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fn DirectedGraph::new() -> DirectedGraph

A new empty graph.

DirectedGraph::node_connected_subgraph_view

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fn DirectedGraph::node_connected_subgraph_view(self : DirectedGraph, name : String) -> DirectedGraph

The sub graph induced by the nodes that can reach the given node.

This is the port of dowhy.graph.node_connected_subgraph_view, which keeps the nodes of the graph that have a path to the given node.

DirectedGraph::node_count

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fn DirectedGraph::node_count(self : DirectedGraph) -> Int

The number of nodes.

DirectedGraph::node_list

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fn DirectedGraph::node_list(self : DirectedGraph) -> Array[String]

The nodes of the graph in insertion order.

DirectedGraph::ordered_predecessors

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fn DirectedGraph::ordered_predecessors(self : DirectedGraph, name : String) -> Array[String]

The predecessors of a node, sorted by their name.

This is the port of dowhy.graph.get_ordered_predecessors. The order of the predecessors is not reliable otherwise, and the columns of a data frame are picked by the parents of a node.

DirectedGraph::predecessors

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fn DirectedGraph::predecessors(self : DirectedGraph, name : String) -> Array[String]

The parents of a node.

The returned array is a copy, so a caller may modify it without changing the graph.

DirectedGraph::remove_edge

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fn DirectedGraph::remove_edge(self : DirectedGraph, from : String, to : String) -> Unit

Remove an edge from the graph.

DirectedGraph::remove_node

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fn DirectedGraph::remove_node(self : DirectedGraph, name : String) -> Unit

Remove the node with the given name and every edge that touches it.

This is the port of networkx.DiGraph.remove_node. Removing a node that is not in the graph does nothing.

DirectedGraph::subgraph

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fn DirectedGraph::subgraph(self : DirectedGraph, names : Array[String]) -> DirectedGraph

The sub graph induced by the given nodes.

DirectedGraph::successors

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fn DirectedGraph::successors(self : DirectedGraph, name : String) -> Array[String]

The children of a node.

The returned array is a copy, so a caller may modify it without changing the graph.

DirectedGraph::to_dot

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fn DirectedGraph::to_dot(self : DirectedGraph) -> String

The graph in Graphviz DOT format.

DirectedGraph::topological_order

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fn DirectedGraph::topological_order(self : DirectedGraph) -> Array[String]

A topological order of the graph.

Returns an empty array when the graph has a cycle.

build_graph

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fn build_graph(action_nodes : Array[String], outcome_nodes : Array[String], common_cause_nodes : Array[String], instrument_nodes : Array[String], effect_modifier_nodes : Array[String], mediator_nodes : Array[String]) -> DirectedGraph

Build a graph from the semantic role of the variables.

d_separated

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fn d_separated(graph : DirectedGraph, x : Array[String], y : Array[String], z : Array[String]) -> Bool

Whether x and y are d-separated given z.

Returns true when the two node sets are separated by the conditioning set.

The traversal follows the classical "active path" characterisation: a path is active given z when every non collider on it is outside z and every collider on it is in z or has a descendant in z. The descendant test is what the ancestor set of z provides.

do_surgery

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fn do_surgery(graph : DirectedGraph, node_names : Array[String], remove_outgoing_edges : Bool, remove_incoming_edges : Bool, target_node_names : Array[String], remove_only_direct_edges_to_target : Bool) -> DirectedGraph

A graph with the edges of the given nodes removed.

remove_outgoing_edges drops every edge leaving the focal nodes;
remove_incoming_edges drops every edge entering the focal nodes;
remove_only_direct_edges_to_target restricts the removal to the edges that point at target_node_names.

get_all_directed_paths

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fn get_all_directed_paths(graph : DirectedGraph, nodes1 : Array[String], nodes2 : Array[String]) -> Array[Array[String]]

The directed paths between two singleton node sets.

get_backdoor_paths

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fn get_backdoor_paths(graph : DirectedGraph, nodes1 : Array[String], nodes2 : Array[String]) -> Array[Array[String]]

The backdoor paths between two node sets.

A path is a backdoor path when its first edge points into the treatment.

get_instruments

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fn get_instruments(graph : DirectedGraph, treatment_nodes : Array[String], outcome_nodes : Array[String]) -> Array[String]

The instruments of the treatment, following dowhy.graph.get_instruments.

get_proper_backdoor_graph

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fn get_proper_backdoor_graph(graph : DirectedGraph, action_nodes : Array[String], outcome_nodes : Array[String]) -> DirectedGraph

The proper backdoor graph of the given causal graph.

get_proper_causal_path_nodes

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fn get_proper_causal_path_nodes(graph : DirectedGraph, action_nodes : Array[String], outcome_nodes : Array[String]) -> @moonbitlang/core/set.Set
Set[String]

The nodes that lie on a proper causal path from the actions to the outcomes, as defined by van der Zander et al.

has_directed_path

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fn has_directed_path(graph : DirectedGraph, action_nodes : Array[String], outcome_nodes : Array[String]) -> Bool

Whether every treatment has a directed path to an outcome and vice versa.

is_blocked

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fn is_blocked(graph : DirectedGraph, path : Array[String], conditioned_nodes : Array[String]) -> Bool

Whether a path is blocked by the given conditioning set.

Source Files

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MoonBit port of DoWhy 0.14 (PyWhy). Causal graph construction and analysis, effect identification (backdoor / frontdoor / complete adjustment), estimation (linear regression, generalized linear model, propensity score weighting / matching / stratification, doubly robust, instrumental variable, two stage, regression discontinuity), refutation (bootstrap, placebo treatment, data subset, dummy outcome, unobserved common cause), intervention sampling (do samplers), temporal graphs, causal discovery (the PC algorithm with the Fisher-z test) and the portable core of the graphical causal models (the p-value merging, the bootstrap confidence intervals, the Kullback-Leibler divergences, the density estimators, the Shapley values, the independence tests, the prediction models, the causal mechanisms, the fitting of a causal model, the what-if queries (the interventional samples, the counterfactual samples and the average causal effect), the validation layer (the kernel based conditional independence test, the validation of a causal structure and the validation of an invertible model) and the falsification layer (the local Markov condition, the pairwise dependencies, the parental d-separations, the causal minimality and the permutation test of a graph) and the model evaluation layer (the normalised mean squared error, the continuous ranked probability score, the performance of the causal mechanisms, the test of the invertibility assumption and the report of the evaluation) and the distribution change layer (the test of the change of a mechanism, the attribution of the change of the distribution of a target node and the scores of the change of the distribution of every node). Pure MoonBit with zero external runtime dependencies.

causal-inferencedowhycausal-graphcausal-discoverybackdoorfrontdoorpropensity-scoretreatment-effectrefutationidentifiersestimatorsshapley-valuesindependence-teststructural-causal-modelcausal-mechanismwhat-ifcounterfactual

riantr

MIT

gitee.com/ren-yongxiang/moonbit_doWhy

Updated 2 hours ago

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#CausalGraph

#CausalGraph::add_missing_nodes_as_common_causes

#CausalGraph::add_unobserved_common_cause

#CausalGraph::all_observed

#CausalGraph::check_dseparation

#CausalGraph::check_valid_backdoor_set

#CausalGraph::check_valid_frontdoor_set

#CausalGraph::check_valid_mediation_set

#CausalGraph::directed_graph

#CausalGraph::do_surgery

#CausalGraph::get_adjacency_matrix

#CausalGraph::get_all_directed_paths

#CausalGraph::get_all_nodes

#CausalGraph::get_ancestors

#CausalGraph::get_common_causes

#CausalGraph::get_descendants

#CausalGraph::get_effect_modifiers

#CausalGraph::get_instruments

#CausalGraph::get_parents

#CausalGraph::has_directed_path

#CausalGraph::is_observed

#CausalGraph::new

#CausalGraph::observed_nodes

#CausalGraph::outcomes

#CausalGraph::treatments

#DirectedGraph

#DirectedGraph::add_edge

#DirectedGraph::add_node

#DirectedGraph::all_simple_paths

#DirectedGraph::all_simple_paths_undirected

#DirectedGraph::ancestors

#DirectedGraph::ancestors_of

#DirectedGraph::copy

#DirectedGraph::descendants

#DirectedGraph::descendants_of

#DirectedGraph::edge_count

#DirectedGraph::edge_list

#DirectedGraph::has_edge

#DirectedGraph::has_node

#DirectedGraph::has_path

#DirectedGraph::is_dag

#DirectedGraph::new

#DirectedGraph::node_connected_subgraph_view

#DirectedGraph::node_count

#DirectedGraph::node_list

#DirectedGraph::ordered_predecessors

#DirectedGraph::predecessors

#DirectedGraph::remove_edge

#DirectedGraph::remove_node

#DirectedGraph::subgraph

#DirectedGraph::successors

#DirectedGraph::to_dot

#DirectedGraph::topological_order

#build_graph

#d_separated

#do_surgery

#get_all_directed_paths

#get_backdoor_paths

#get_instruments

#get_proper_backdoor_graph

#get_proper_causal_path_nodes

#has_directed_path

#is_blocked

Source Files

CausalGraph

CausalGraph::add_missing_nodes_as_common_causes

CausalGraph::add_unobserved_common_cause

CausalGraph::all_observed

CausalGraph::check_dseparation

CausalGraph::check_valid_backdoor_set

CausalGraph::check_valid_frontdoor_set

CausalGraph::check_valid_mediation_set

CausalGraph::directed_graph

CausalGraph::do_surgery

CausalGraph::get_adjacency_matrix

CausalGraph::get_all_directed_paths

CausalGraph::get_all_nodes

CausalGraph::get_ancestors

CausalGraph::get_common_causes

CausalGraph::get_descendants

CausalGraph::get_effect_modifiers

CausalGraph::get_instruments

CausalGraph::get_parents

CausalGraph::has_directed_path

CausalGraph::is_observed

CausalGraph::new

CausalGraph::observed_nodes

CausalGraph::outcomes

CausalGraph::treatments

DirectedGraph

DirectedGraph::add_edge

DirectedGraph::add_node

DirectedGraph::all_simple_paths

DirectedGraph::all_simple_paths_undirected

DirectedGraph::ancestors

DirectedGraph::ancestors_of

DirectedGraph::copy

DirectedGraph::descendants

DirectedGraph::descendants_of

DirectedGraph::edge_count

DirectedGraph::edge_list

DirectedGraph::has_edge

DirectedGraph::has_node

DirectedGraph::has_path

DirectedGraph::is_dag

DirectedGraph::new

DirectedGraph::node_connected_subgraph_view

DirectedGraph::node_count

DirectedGraph::node_list

DirectedGraph::ordered_predecessors

DirectedGraph::predecessors

DirectedGraph::remove_edge

DirectedGraph::remove_node

DirectedGraph::subgraph

DirectedGraph::successors

DirectedGraph::to_dot

DirectedGraph::topological_order

build_graph

d_separated

do_surgery

get_all_directed_paths

get_backdoor_paths

get_instruments

get_proper_backdoor_graph

get_proper_causal_path_nodes

has_directed_path

is_blocked