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Capabilities

The full inventory of what Antecedent implements, area by area. The README carries the highlights; this page is the reference list. For what is deliberately not implemented, see Comparison.

Graphs

Supported graph classes:

  • DAG;
  • ADMG;
  • CPDAG;
  • PAG;
  • temporal DAG;
  • temporal CPDAG;
  • temporal PAG.

Graph operations:

  • d-separation;
  • m-separation;
  • districts;
  • latent projection;
  • Markov-equivalence completions;
  • definite-status separation;
  • temporal unfolding;
  • intervention overlays.

Static and temporal graphs have separate semantics. A static graph is not interpreted as temporal by default.

Graph interchange is available through NetworkX, DOT, JSON, GML, and versioned CBOR artifacts.

Discovery

Static

  • PC
  • FCI
  • RFCI
  • GES
  • DirectLiNGAM
  • NOTEARS

Temporal and multi-context

  • PCMCI
  • PCMCI+
  • LPCMCI
  • J-PCMCI+
  • regime-specific RPCMCI workflows

Bayesian structure learning

  • exact DAG posterior;
  • order MCMC;
  • structure MCMC;
  • CI-screened graph posterior;
  • DBN posterior.

Posterior graph samples can be propagated into downstream effect analyses.

Conditional independence tests

  • partial correlation;
  • weighted and robust partial correlation;
  • regression CI;
  • k-nearest-neighbour CI;
  • mixed k-nearest-neighbour CI;
  • symbolic conditional mutual information;
  • GPDC;
  • G²;
  • oracle tests;
  • Bayesian CI tests.

Multiplicity corrections include BH, BY, Bonferroni, and Holm.

Discovery stability tools include block bootstrap, lag and threshold sensitivity, orientation stability, environment holdout, synthetic-null checks, and permutation or phase-randomized surrogates.

Identification

Antecedent reports whether a query is:

  • nonparametrically identified;
  • partially identified;
  • graph-dependent;
  • not identified.

Implemented identification strategies:

  • backdoor adjustment;
  • efficient backdoor adjustment;
  • front-door identification;
  • instrumental variables;
  • sharp regression discontinuity;
  • ID and IDC for DAGs and ADMGs;
  • hedge certificates;
  • nonparametric path-specific identification;
  • generalized adjustment for partial graphs;
  • unfolded temporal backdoor;
  • temporal mediation.

AutoIdentifier reports applicable strategies. It does not silently choose an estimator.

For PAGs, Antecedent uses identification envelopes or explicit graph completions. Full PAG-native ID and IDC are outside the supported scope.

Estimation

Frequentist

  • linear and generalized-linear outcome regression;
  • g-computation;
  • inverse probability weighting;
  • propensity matching;
  • covariate-distance matching;
  • stratification;
  • AIPW;
  • front-door two-stage estimation;
  • Wald estimation;
  • 2SLS;
  • sharp local-linear regression discontinuity;
  • linear conditional effect models;
  • temporal adjustment;
  • temporal mediation;
  • functional plug-in estimation.

Bayesian

  • Bayesian g-computation;
  • temporal Bayesian g-computation;
  • conjugate Gaussian models;
  • Laplace GLM approximation;
  • HMC GLMs;
  • graph-by-effect posterior envelopes;
  • same-design prior transfer;
  • effect-level and mapped prior transfer;
  • prior catalogs and compatibility filtering;
  • power-prior mixtures;
  • conflict-sensitive prior weighting;
  • transport policies across compatible designs.

Unidentified graph-posterior mass is retained rather than silently renormalized away.

Interventions and counterfactuals

Antecedent includes a structural causal model layer.

Supported mechanisms:

  • linear-Gaussian models;
  • constant mechanisms;
  • discrete mechanisms;
  • hierarchical linear and generalized-linear models;
  • Minnesota BVAR;
  • linear Gaussian state-space models;
  • Gaussian-process mechanisms.

Supported interventions:

  • hard interventions;
  • soft interventions;
  • stochastic interventions;
  • sequenced interventions;
  • temporal policies;
  • dynamic policies;
  • mechanism overrides.

Do-sampling methods include weighting, KDE, and MCMC.

Counterfactual support:

  • abduction–action–prediction;
  • nested counterfactuals;
  • temporal trajectories;
  • unit-level counterfactual analysis.

Attribution and diagnostics

Antecedent can analyze:

  • anomalous outcomes;
  • distribution shifts;
  • structural changes;
  • mechanism changes;
  • change points;
  • unit-level change;
  • path contributions;
  • arrow strength;
  • feature relevance;
  • root-cause rankings.

Implemented techniques:

  • likelihood-ratio tests;
  • mean-difference tests;
  • classifier-based tests;
  • MMD;
  • Gaussian KL divergence;
  • CUSUM-style scans;
  • Shapley attribution;
  • coalition caching.

Validation and sensitivity

Estimate validation:

  • placebo refuters;
  • random common-cause refuters;
  • unobserved common-cause refuters;
  • bootstrap refuters;
  • data-subset refuters;
  • dummy-outcome refuters;
  • overlap diagnostics;
  • E-values;
  • graph refutation.

Sensitivity methods:

  • linear sensitivity;
  • partial-linear sensitivity;
  • nonparametric sensitivity;
  • Reisz sensitivity.

Bayesian validation:

  • prior predictive checks;
  • prior sensitivity;
  • MCMC diagnostics;
  • simulation-based calibration hooks.

Resampling support:

  • IID bootstrap;
  • Bayesian bootstrap;
  • moving-block bootstrap;
  • circular-block bootstrap;
  • column permutation;
  • phase-randomized surrogates.

Experimental design

Antecedent can rank candidate actions such as:

  • measuring a variable;
  • intervening on a variable;
  • observing an environment;
  • changing a sampling plan.

Ranking criteria:

  • expected information gain;
  • probability of identification;
  • expected effect-interval width;
  • decision utility.

The design layer supports batched Monte Carlo evaluation, common random numbers, and early stopping.

Incremental state

CausalState supports stateful and online workflows.

Available components:

  • explicit invalidation;
  • incremental OLS;
  • streaming covariance;
  • particle-filter state-space models;
  • local score caches;
  • rolling mechanism diagnostics;
  • configurable cache budgets;
  • prepared analyses;
  • progressive and cancellable execution;
  • adaptive resampling.

Invalidation does not automatically rerun an analysis.

Data support

Antecedent supports:

  • tabular data;
  • time series;
  • panel data;
  • multi-environment data;
  • event data converted into temporal frames.

Python interfaces support NumPy, pandas, and Arrow CDI. Rust uses TableView.

Artifacts

Versioned artifacts:

  • graphs;
  • graph posteriors;
  • model bundles;
  • analysis traces;
  • causal state.

Artifacts use schema-versioned CBOR containers with optional Zstandard-compressed sections, selective reads, and memory-mapped access.