Antecedent

Antecedent is an identification-first causal inference engine for Python (with a native Rust core). It takes an analysis from causal structure through estimation, diagnostics, interventions, and counterfactuals — without silently treating discovered graphs as ground truth.

Use it when you need a causal effect you can defend: identification is checked before anything is estimated, refuters run against the estimate by default, and uncertainty about the causal graph — a CPDAG, a PAG, a posterior over graphs — is carried through to the effect instead of being resolved by fiat.

Requires CPython 3.11–3.14. Install from PyPI:

pip install antecedent

Paste this block and run it. It simulates a confounded dataset, checks that the effect is identified, estimates it, and runs refuters against the estimate:

import numpy as np
from antecedent import AverageEffect, analyze

rng = np.random.default_rng(0)
n = 2000
season = rng.normal(size=n)                               # confounder
price = 0.7 * season + rng.normal(size=n)                 # treatment
sales = 1.5 * price + 2.0 * season + rng.normal(size=n)   # outcome, true effect = 1.5

result = analyze(
    data={"season": season, "price": price, "sales": sales},
    graph=[("season", "price"), ("season", "sales"), ("price", "sales")],
    query=AverageEffect(treatment="price", outcome="sales"),
)

print(result.identification)   # NonparametricallyIdentified via backdoor.adjustment, adjusting for season
print(result.estimate)         # ate=1.48, analytic and bootstrap standard errors
print(result.validation)       # refuters ran and passed

The same analyze() call scales from this to temporal pulse effects, Bayesian graph-posterior mixtures that report unidentified structure mass, mediation, counterfactuals, and root-cause attribution — see the project README for worked examples and the documentation for the full API.

Development

For local development you need a Rust 1.85 toolchain. CI builds and smoke-tests wheels for the supported CPython range on Linux x86_64/aarch64 (manylinux), macOS arm64, and Windows x86_64 (default faer path; no system BLAS). Tagged releases publish wheels to PyPI and GitHub Release assets (see docs/development.md).

cd python
uv venv && source .venv/bin/activate
uv sync --group dev
maturin develop
pytest

Lint and types (local gate only — not part of wheel CI):

bash ../scripts/gate_python_lint.sh   # ruff check/format + mypy

Public API

Primary entry point is the OO facade:

import antecedent

g = antecedent.Dag.from_edges(["z", "t", "y"], [("z", "t"), ("z", "y"), ("t", "y")])
result = antecedent.analyze(
    data,  # dict[str, array] or pandas DataFrame
    graph=g,  # or an edge list
    query=antecedent.AverageEffect(treatment="t", outcome="y"),
    inference=antecedent.Frequentist(),  # or antecedent.Bayesian(...)
)
print(result.identification, result.estimate, result.validation)

# Identify without estimating:
id_only = antecedent.identify(
    graph=g,
    query=antecedent.AverageEffect(treatment="t", outcome="y"),
    identifier=antecedent.Identifier.BACKDOOR_ADJUSTMENT,
)

gcm = antecedent.model.fit_gcm(["z", "t", "y"], columns, list(g.edges()))
draws = gcm.sample_do({"t": 1.0}, n=200)

# Or discover then fit (never invents orientations; refuses incomplete PAG/FCI):
fitted, edges = antecedent.gcm.fit_gcm_discovered(
    data, discovery=antecedent.discovery.PC(alpha=0.05)
)

The root namespace is frozen to 41 names; everything else is reached through a stage module (antecedent.discovery, antecedent.priors, antecedent.errors, …).

Also exposed:

  • Typed graphs: Dag / Cpdag / Pag / Admg / TemporalDag (d_separated / latent_project on Dag; m_separated on Admg / Pag)
  • Identifier / Estimator enums (wire ids) plus string kwargs
  • identify(graph=…, query=AverageEffect(…)) — identify without estimating
  • Queries: AverageEffect, MediationEffect, Counterfactual, PulseEffect, SustainedEffect, InterventionalDistribution, PathSpecificEffect, ConditionalEffect, TemporalMediationEffect
  • antecedent.discovery — PC, GES, LiNGAM, NOTEARS, FCI/RFCI, PCMCI family, Bayesian posteriors
  • antecedent.validation.validate_pcmci_* — discovery stability (block bootstrap, FPR, grids, …)
  • antecedent.model / antecedent.counterfactual — FittedGcm, sample_do, counterfactual_ite
  • antecedent.population — PopulationRegistry / target_* for named predicates and custom-distribution IPW
  • antecedent.gcm — fit_gcm_discovered / attribute_*_discovered discover-then-attribute composition
  • antecedent.state.CausalState — incremental state with retained batches, events, suff-stats, particle filter
  • refute=True|"full"|"placebo"|False on static and temporal analyze
  • RD: estimator="rd.sharp" with running_variable / cutoff / bandwidth
  • Graph interchange on the classes: Dag.from_dot / .to_dot and the JSON / GML / NetworkX peers
  • Design / state examples: examples/python/rank_designs.py, examples/python/causal_state_workflow.py (see ADR 0016 — no auto-rerun); catalog in examples/README.md

Build artifacts (_native.*.so) are gitignored; always maturin develop (or install a wheel) on a fresh checkout.

Typed exceptions (CausalError and subclasses) mirror Rust CausalError categories. The native module antecedent._native remains available for advanced FFI use (including the flat AteAnalysisResult DTO; prefer nested AnalysisResult).

Project links

Data verified by PyPI on Jul 30, 2026
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  • Released:
Newer release available (1.5.0)

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GitHub Statistics

Data verified by PyPI on Jul 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License, MIT License (MIT OR Apache-2.0)

Requires

Python >=3.11

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1.5.0

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1.4.0

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1.3.0

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1.2.0

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1.1.0

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0.9.0

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0.6.1

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0.6.0

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0.5.2

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0.5.1

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0.5.0

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0.4.1 This release

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0.1.0

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