Integration API

prepare_for_tmle(g, X, Y; node_names=nothing)

Prepare adjustment set from causal graph for use with TMLE.jl.

Arguments

• g: Directed acyclic graph (DiGraph or CausalGraph)
• X: Treatment node (integer or symbol)
• Y: Outcome node (integer or symbol)
• node_names: Optional mapping from node indices to symbols/names. Accepts Dict{Int,Symbol} or a Vector of names (index i names node i). If provided, returns symbol names; otherwise returns node indices. For CausalGraph, node names are automatically extracted from graph properties if not provided.

Returns

• adj_set: Vector of confounders (symbols or integers) ready for TMLE.jl
• is_identifiable: Boolean indicating if effect is identifiable via backdoor adjustment

Examples

using CausalDynamics, TMLE, DataFrames

# Create graph: Z → X → Y, Z → Y
g = DiGraph(3)
add_edge!(g, 1, 2)  # Z → X
add_edge!(g, 1, 3)  # Z → Y
add_edge!(g, 2, 3)  # X → Y

# With node names (Dict or vector indexed by node)
node_names = Dict(1 => :Z, 2 => :X, 3 => :Y)
confounders, identifiable = prepare_for_tmle(g, 2, 3; node_names=node_names)
# Returns: ([:Z], true)

confounders, identifiable = prepare_for_tmle(g, 2, 3; node_names=[:Z, :X, :Y])
# Returns: ([:Z], true)

# Without node names (returns indices)
confounders, identifiable = prepare_for_tmle(g, 2, 3)
# Returns: ([1], true)

# With CausalGraph (auto-detects node names)
g2 = CausalGraph(3)
add_edge!(g2, 1, 2)
add_edge!(g2, 1, 3)
add_edge!(g2, 2, 3)
set_node_prop!(g2, 1, :name, :Z)
set_node_prop!(g2, 2, :name, :X)
set_node_prop!(g2, 3, :name, :Y)
confounders, identifiable = prepare_for_tmle(g2, 2, 3)  # Auto-uses node names
# Returns: ([:Z], true)

See Also

• backdoor_adjustment_set: Find backdoor adjustment set
• is_backdoor_adjustable: Check if backdoor adjustment is possible

estimate_effect(g, data, X, Y; node_names=nothing, method=:tmle, kwargs...)
estimate_effect(g, X, Y; method=:tmle, kwargs...)

Estimate causal effect using identified adjustment set and TMLE.jl.

This function combines identification (CausalDynamics.jl) with estimation (TMLE.jl) in a single workflow. For CausalGraph, data and node names can be automatically extracted from graph properties.

Arguments

• g: Directed acyclic graph (DiGraph or CausalGraph)
• data: DataFrame or table with observed data (optional for CausalGraph if data is attached)
• X: Treatment variable (integer index or symbol/name)
• Y: Outcome variable (integer index or symbol/name)
• node_names: Optional mapping from node indices to symbols/names. For CausalGraph, node names are automatically extracted from graph properties if not provided.
• method: Estimation method (currently only :tmle supported)
• kwargs...: Additional arguments passed to TMLE.jl estimation function

Returns

• TMLE.jl result object with effect estimate, confidence intervals, etc.

Examples

using CausalDynamics, TMLE, DataFrames

# Create graph: Z → X → Y, Z → Y
g = DiGraph(3)
add_edge!(g, 1, 2)  # Z → X
add_edge!(g, 1, 3)  # Z → Y
add_edge!(g, 2, 3)  # X → Y

# Create data
data = DataFrame(
    Z = randn(100),
    X = rand([0, 1], 100),
    Y = randn(100)
)

# Estimate effect with node names
node_names = Dict(1 => :Z, 2 => :X, 3 => :Y)
result = estimate_effect(g, data, 2, 3; node_names=node_names)

# With CausalGraph - data and names attached
g2 = CausalGraph(3)
add_edge!(g2, 1, 2)
add_edge!(g2, 1, 3)
add_edge!(g2, 2, 3)
set_node_prop!(g2, 1, :name, :Z)
set_node_prop!(g2, 2, :name, :X)
set_node_prop!(g2, 3, :name, :Y)
attach_data!(g2, data)
result = estimate_effect(g2, 2, 3)  # Auto-uses attached data and names

Notes

• Requires TMLE.jl to be loaded: using TMLE
• If no adjustment set is found, a warning is issued but estimation may still proceed
• Additional TMLE.jl arguments can be passed via kwargs...
• For CausalGraph, if data is attached and node names are set, they are used automatically

See Also

• prepare_for_tmle: Prepare adjustment set for TMLE.jl
• backdoor_adjustment_set: Find backdoor adjustment set
• attach_data!: Attach data to CausalGraph

get_tmle_confounders(g, X, Y; node_names=nothing)

Get confounders for TMLE.jl from causal graph.

Convenience function that returns just the confounders vector, without the identifiability check.

Arguments

• g: Directed acyclic graph
• X: Treatment node (integer or symbol)
• Y: Outcome node (integer or symbol)
• node_names: Optional mapping from node indices to symbols/names

Returns

• Vector of confounders (symbols or integers) ready for TMLE.jl

Examples

using CausalDynamics

g = DiGraph(3)
add_edge!(g, 1, 2)
add_edge!(g, 1, 3)
add_edge!(g, 2, 3)

node_names = Dict(1 => :Z, 2 => :X, 3 => :Y)
confounders = get_tmle_confounders(g, 2, 3; node_names=node_names)
# Returns: [:Z]

See Also

• prepare_for_tmle: Full preparation with identifiability check
• backdoor_adjustment_set: Find backdoor adjustment set

prepare_for_rxinfer(g, X, Y; node_names=nothing, data=nothing)

Prepare causal graph and data for use with RxInfer.jl.

RxInfer.jl uses various data formats and requires explicit variable specifications. This function extracts the necessary information from a CausalGraph or prepares it from a plain graph.

Arguments

• g: Directed acyclic graph (DiGraph or CausalGraph)
• X::Int: Treatment node index
• Y::Int: Outcome node index
• node_names::Union{Dict{Int, Symbol}, Nothing}: Optional mapping from node indices to names. For CausalGraph, automatically extracted if not provided.
• data: Optional data (DataFrame, NamedTuple, or other). For CausalGraph, automatically extracted if attached and not provided.

Returns

• NamedTuple with fields:
  • graph: The underlying graph (DiGraph)
  • treatment: Treatment variable name (Symbol) or index (Int)
  • outcome: Outcome variable name (Symbol) or index (Int)
  • confounders: Vector of confounder names (Symbol) or indices (Int)
  • data: Data in format compatible with RxInfer
  • node_names: Dictionary mapping node indices to names
  • is_identifiable: Boolean indicating if effect is identifiable

Examples

using CausalDynamics, DataFrames

# With CausalGraph
g = CausalGraph(3)
add_edge!(g, 1, 2)
add_edge!(g, 1, 3)
add_edge!(g, 2, 3)
set_node_prop!(g, 1, :name, :Z)
set_node_prop!(g, 2, :name, :X)
set_node_prop!(g, 3, :name, :Y)
data = DataFrame(Z=randn(100), X=rand([0,1], 100), Y=randn(100))
attach_data!(g, data)

spec = prepare_for_rxinfer(g, 2, 3)
# Returns: (graph=..., treatment=:X, outcome=:Y, confounders=[:Z], data=..., ...)

# Use in RxInfer model
using RxInfer
# Use spec.treatment, spec.outcome, spec.confounders, spec.data

Notes

• Data is returned as-is - RxInfer accepts various formats
• Node names are required for meaningful variable names in models
• If node names are not available, indices are used instead
• RxInfer typically uses NamedTuples or dictionaries for data

See Also

• prepare_for_turing: Prepare for Turing.jl
• get_tmle_confounders: Get confounders for TMLE

has_rxinfer() -> Bool

Return true when the CausalDynamicsRxInfer extension is loaded (using RxInfer).

infer_backdoor_effect(g, data, X, Y; kwargs...) -> BackdoorInferenceResult

Backdoor-adjusted variational inference for treatment effect τ (GraphPPL + RxInfer).

Requires using RxInfer before calling. See extension docstring for arguments.

backdoor_graphppl_model(; n_conf=0)

GraphPPL @model generator for the Gaussian backdoor head. Requires using RxInfer.

ppl_data_from_spec(spec; kwargs...) -> NamedTuple

Convert prepare_for_rxinfer output to RxInfer data (outcome, treatment, confounder matrix). Requires using RxInfer.

prepare_for_turing(g, X, Y; node_names=nothing, data=nothing)

Prepare causal graph and data for use with Turing.jl.

Turing.jl uses DataFrames or NamedTuples for data, and requires explicit variable names. This function extracts the necessary information from a CausalGraph or prepares it from a plain graph.

Arguments

• g: Directed acyclic graph (DiGraph or CausalGraph)
• X::Int: Treatment node index
• Y::Int: Outcome node index
• node_names::Union{Dict{Int, Symbol}, Nothing}: Optional mapping from node indices to names. For CausalGraph, automatically extracted if not provided.
• data: Optional data (DataFrame, NamedTuple, or other). For CausalGraph, automatically extracted if attached and not provided.

Returns

• NamedTuple with fields:
  • graph: The underlying graph (DiGraph)
  • treatment: Treatment variable name (Symbol) or index (Int)
  • outcome: Outcome variable name (Symbol) or index (Int)
  • confounders: Vector of confounder names (Symbol) or indices (Int)
  • data: Data in format compatible with Turing
  • node_names: Dictionary mapping node indices to names
  • is_identifiable: Boolean indicating if effect is identifiable

Examples

using CausalDynamics, DataFrames

# With CausalGraph
g = CausalGraph(3)
add_edge!(g, 1, 2)
add_edge!(g, 1, 3)
add_edge!(g, 2, 3)
set_node_prop!(g, 1, :name, :Z)
set_node_prop!(g, 2, :name, :X)
set_node_prop!(g, 3, :name, :Y)
data = DataFrame(Z=randn(100), X=rand([0,1], 100), Y=randn(100))
attach_data!(g, data)

spec = prepare_for_turing(g, 2, 3)
# Returns: (graph=..., treatment=:X, outcome=:Y, confounders=[:Z], data=..., ...)

# Use in Turing model
using Turing
@model function causal_model(spec)
    # Use spec.treatment, spec.outcome, spec.confounders, spec.data
    # ...
end

Notes

• Data is returned as-is (DataFrame or NamedTuple) - Turing accepts both
• Node names are required for meaningful variable names in models
• If node names are not available, indices are used instead

See Also

• prepare_for_rxinfer: Prepare for RxInfer.jl
• get_tmle_confounders: Get confounders for TMLE

get_data_for_ppl(g; format=:dataframe)

Extract data from CausalGraph in a format suitable for probabilistic programming.

Arguments

• g::CausalGraph: The causal graph with attached data
• format::Symbol: Desired output format (:dataframe, :namedtuple, :dict)

Returns

• Data in the requested format, or nothing if no data attached

Examples

using CausalDynamics, DataFrames

g = CausalGraph(3)
data = DataFrame(Z=randn(100), X=rand([0,1], 100), Y=randn(100))
attach_data!(g, data)

# Get as DataFrame (default)
df = get_data_for_ppl(g)  # DataFrame

# Get as NamedTuple (for Turing/RxInfer)
nt = get_data_for_ppl(g; format=:namedtuple)  # NamedTuple

# Get as Dict
dict = get_data_for_ppl(g; format=:dict)  # Dict

Notes

• Requires data to be attached to the graph
• Format conversion may have performance implications for large datasets
• NamedTuple format is efficient for Turing.jl
• Dict format is flexible for various PPL packages

See Also

• get_data: Get data as-is
• attach_data!: Attach data to graph

has_associations() -> Bool

Return true when the CausalDynamicsAssociationsExt extension is loaded (using Associations).

prepare_from_discovery(g, treatment, outcome; node_names=nothing, complete=false)

Run backdoor identification on a candidate graph from discovery (PC, OCE, domain knowledge).

The input graph is treated as structural hypothesis, not ground truth. Discovery typically returns a CPDAG or lag parent set under faithfulness and sufficiency assumptions.

Returns

• confounders: adjustment set (symbols if node_names given, else node indices)
• identifiable: whether a backdoor adjustment set exists

See Also

• prepare_for_tmle
• backdoor_adjustment_set

cpdag_to_dag(g::Graphs.DiGraph) -> Graphs.DiGraph

Break remaining bidirectional edges in a PC output (CPDAG) to obtain a DAG.

When both i → j and j → i are present, drops j → i if i < j. Discovery outputs may still be only partially oriented; this is a pragmatic heuristic before calling backdoor identification.

oce_parents_to_temporal_spec(parents, variables::AbstractVector{Symbol}) -> TemporalDAGSpec

Convert OCE OCESelectedParents output to a TemporalDAGSpec.

Arguments

• parents: vector of parent selections (one per variable), e.g. from infer_graph(OCE(), ts)
• variables: symbol name for each series index (variables[i] is variable i)

Lag convention

OCE stores embedding lags in parents_τs (typically negative, e.g. -1 for xⱼ(-1)). These map to LaggedEdge lags as lag = abs(τ), so τ = -1 becomes lag = 1 (parent at t-1 causes child at t), matching Ch. 28 unrolling.

digraph_with_names(g::AbstractGraph, names::AbstractVector{Symbol}) -> CausalGraph

Wrap g in a CausalGraph and attach :name properties from names.

length(names) must equal nv(g).

infer_pc_graph(data, names; kwargs...) -> CausalGraph

PC discovery with node names attached. Requires using Associations.

infer_oce_temporal_spec(ts, variables; kwargs...) -> TemporalDAGSpec

OCE discovery converted to TemporalDAGSpec. Requires using Associations.

discover_and_prepare(data, treatment, outcome; method=:pc, kwargs...)

Discover a graph then call prepare_from_discovery. Requires using Associations.

AbstractCausalIntervention

Root type for all do(·) interventions in CausalDynamics: static SCM assignments, discrete-time CDM sequences/policies, and continuous-time CDM interventions.

Dispatch on the concrete subtype and the model (SCM / DiscreteTimeCDM / ContinuousCDMSpec) determines how the intervention is applied.

AbstractContinuousIntervention

Continuous-time interventions for solve_cdm / SciML: pins, initial conditions, soft forces, and RHS replacements (continuous-CDM taxonomy).

ContinuousCDMSpec

Named continuous-time CDM: maps endogenous symbols to state-vector indices, with optional parent sets for each coordinate (ODE / continuous causal graph).

Use with ode_problem_cdm and solve_cdm when the mechanism is an ODE rather than a DiscreteTimeCDM.

continuous_cdm_graph(spec::ContinuousCDMSpec) -> SimpleDiGraph

Directed graph with an edge p → child for each declared parent set entry.

with_parents(spec::ContinuousCDMSpec, parents) -> ContinuousCDMSpec

Return a copy of spec with parent sets replaced by parents (same validation as ContinuousCDMSpec).

ranked_variables_to_parents(ranking, variables; target, max_parents=2) -> Dict

Build a single-target parent map from a variable ranking (ranking[i] is the rank of variables[i], lower is better; omit target).

has_sciml() -> Bool

Return true when the CausalDynamicsSciMLExt extension is loaded (using OrdinaryDiffEq).

ode_problem_cdm(spec, rhs!, u0, tspan, p; intervention=nothing, kwargs...)

Build an ODEProblem with length(u0) == length(spec.variables). Requires using OrdinaryDiffEq.

solve_cdm(spec, rhs!, u0, tspan, p; intervention=nothing, kwargs...) -> sol

Integrate a continuous CDM and return the SciML solution. Requires using OrdinaryDiffEq.

terminal_state(spec::ContinuousCDMSpec, sol) -> NamedTuple

Terminal endogenous state keyed by symbol. Requires using OrdinaryDiffEq.

state_series(spec::ContinuousCDMSpec, sol) -> NamedTuple

Time series of each endogenous variable. Requires using OrdinaryDiffEq.

interventional_rhs(rhs!, spec::ContinuousCDMSpec, intervention)

Wrap a continuous RHS for a continuous-CDM intervention. Requires using OrdinaryDiffEq.

intervention_callback(spec, intervention)

SciML DiscreteCallback / CallbackSet maintaining hard pins. Requires using OrdinaryDiffEq.

DoPin

Hard pin: set the initial condition to value and hold the coordinate fixed (ẋᵏ := 0). SciML applies this without mutating u inside the RHS; a DiscreteCallback reasserts the value after accepted steps to control drift.

DoInitialCondition

Set only the initial value of variable to value (leave the RHS unchanged).

Corresponds to do(x₀ᵏ := ξ) in continuous-time CDMs [@peters2022causal].

DoForce

Add a soft restoring term -κ (u - target) to the RHS of variable.

DoRhs

Replace the RHS of variable with du_fn(u, p, t) -> Real.

do_pin(variable, value)

Hard pin for continuous CDMs (DoPin). Also accepted: static DoIntervention as a pin for backwards compatibility.

do_ic(variable, value)

Initial-condition intervention do(x₀ := value).

do_force(variable, target; κ = 1.0)

Soft force toward target with strength κ.

do_rhs(variable, du_fn)

Replace the coordinate RHS of variable with du_fn(u, p, t).

forward_sensitivity_cdm(spec, rhs!, u0, tspan, p; kwargs...) -> sol

Forward local sensitivity of a continuous CDM via SciMLSensitivity. Requires using OrdinaryDiffEq, SciMLSensitivity.

interventional_embedding_entropy(x, y; p=1, k=2, theiler=nothing, n_delta=10, mi=:auto) -> Float64

Compute IEE from cause series x to effect series y (IntDC ranking score).

Arguments

• x, y: univariate vectors, or d × T matrices (variables × time)
• p: embedding order (delay length)
• k: k-NN order for MI (k ≥ 2)
• theiler: half-width of Theiler window (defaults to p)
• n_delta: number of neighbours used as local perturbation proxies
• mi: mutual-information estimator — :auto (Associations KSG1 when loaded, else reference), :associations (requires using Associations), or :reference (MATLAB-faithful MIknn port for concordance tests)
• backend: deprecated alias for mi (kept for notebook sessions that still expect the old keyword)

References

• Shi et al. (2026), The Innovation; arXiv:2407.01621
• Reference code: https://github.com/smsxiaomayi/IEE

iee_score_matrix(series; variables=nothing, kwargs...) -> Matrix{Float64}

Pairwise IEE matrix for a vector of univariate series (series[i] is variable i). Diagonal is zero. Keyword arguments are forwarded to interventional_embedding_entropy.

iee_to_temporal_spec(scores, variables; threshold, lag=1) -> TemporalDAGSpec

Convert an IEE score matrix into a TemporalDAGSpec by retaining directed edges with score ≥ threshold at lag lag (default 1: parent at t-1).

infer_iee_temporal_spec(series, variables; threshold, kwargs...) -> TemporalDAGSpec

Compute pairwise IEE and threshold into a TemporalDAGSpec.

ODEParentRanking

Result of infer_ode_parents: candidate parent-index sets, their cross-environment instability scores (lower is better), per-variable inclusion scores, and a variable ranking (best first).

infer_ode_parents(times, trajectories, env, target; max_size=2, K=nothing)

Infer an ODEParentRanking for main-effect parent sets of target.

Uses cubic-spline derivatives when DataInterpolations is loaded; otherwise finite differences. Reference method: CausalKinetiX [@pfister2019causalkinetix].

score_ode_parent_sets(times, trajectories, env, target, models; differentiate=nothing)

Score each candidate parent-index set for target coordinate target.

Arguments

• times: length-L observation grid
• trajectories: vector of d × L matrices (one per repetition)
• env: environment id per repetition
• target: 1-based target coordinate
• models: candidate parent-index sets
• differentiate: (t, y) -> dy (defaults to finite_difference_derivative)

ode_parent_ranking_to_continuous_spec(result, variables; max_parents=2) -> ContinuousCDMSpec

Map an ODEParentRanking onto a ContinuousCDMSpec parent map for the ranked target.

candidate_parent_sets(d; max_size=2) -> Vector{Vector{Int}}

All non-empty subsets of {1,…,d} with size at most max_size (1-based indices).

finite_difference_derivative(t, y) -> Vector{Float64}

Central finite differences on possibly irregular grids (one-sided at endpoints).