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 at 49 names through 0.6; 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, InterventionResponse, plus the response family (ResponseCurve, AverageDerivative, PointDerivative, Elasticity, SemiElasticity, DirectionalDerivative, ResponseJacobian)
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).

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

antecedent-0.6.1-cp314-cp314-win_amd64.whl (6.2 MB view details)

Uploaded Aug 20, 2026 CPython 3.14Windows x86-64

antecedent-0.6.1-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.6 MB view details)

Uploaded Aug 20, 2026 CPython 3.14manylinux: glibc 2.17+ x86-64

antecedent-0.6.1-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (6.0 MB view details)

Uploaded Aug 20, 2026 CPython 3.14manylinux: glibc 2.17+ ARM64

antecedent-0.6.1-cp314-cp314-macosx_11_0_arm64.whl (5.6 MB view details)

Uploaded Aug 20, 2026 CPython 3.14macOS 11.0+ ARM64

antecedent-0.6.1-cp313-cp313-win_amd64.whl (6.2 MB view details)

Uploaded Aug 20, 2026 CPython 3.13Windows x86-64

antecedent-0.6.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.7 MB view details)

Uploaded Aug 20, 2026 CPython 3.13manylinux: glibc 2.17+ x86-64

antecedent-0.6.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (6.0 MB view details)

Uploaded Aug 20, 2026 CPython 3.13manylinux: glibc 2.17+ ARM64

antecedent-0.6.1-cp313-cp313-macosx_11_0_arm64.whl (5.6 MB view details)

Uploaded Aug 20, 2026 CPython 3.13macOS 11.0+ ARM64

antecedent-0.6.1-cp312-cp312-win_amd64.whl (6.2 MB view details)

Uploaded Aug 20, 2026 CPython 3.12Windows x86-64

antecedent-0.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.7 MB view details)

Uploaded Aug 20, 2026 CPython 3.12manylinux: glibc 2.17+ x86-64

antecedent-0.6.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (6.0 MB view details)

Uploaded Aug 20, 2026 CPython 3.12manylinux: glibc 2.17+ ARM64

antecedent-0.6.1-cp312-cp312-macosx_11_0_arm64.whl (5.6 MB view details)

Uploaded Aug 20, 2026 CPython 3.12macOS 11.0+ ARM64

antecedent-0.6.1-cp311-cp311-win_amd64.whl (6.2 MB view details)

Uploaded Aug 20, 2026 CPython 3.11Windows x86-64

antecedent-0.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.7 MB view details)

Uploaded Aug 20, 2026 CPython 3.11manylinux: glibc 2.17+ x86-64

antecedent-0.6.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (6.1 MB view details)

Uploaded Aug 20, 2026 CPython 3.11manylinux: glibc 2.17+ ARM64

antecedent-0.6.1-cp311-cp311-macosx_11_0_arm64.whl (5.6 MB view details)

Uploaded Aug 20, 2026 CPython 3.11macOS 11.0+ ARM64

File details

Details for the file antecedent-0.6.1-cp314-cp314-win_amd64.whl.

File metadata

Download URL: antecedent-0.6.1-cp314-cp314-win_amd64.whl
Upload date: Aug 20, 2026
Size: 6.2 MB
Tags: CPython 3.14, Windows x86-64
Uploaded using Trusted Publishing? Yes
Uploaded via: uv/0.12.5 {"installer":{"name":"uv","version":"0.12.5","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for antecedent-0.6.1-cp314-cp314-win_amd64.whl
Algorithm	Hash digest
SHA256	`dfe4d90f56be641682998e6f7698d88d4d5069ca5956b86089e77394e922a3e3`
MD5	`112edfc71b20abdce75722e49e9a05ba`
BLAKE2b-256	`ed45e5b3ed104bb80e0913d124dbc2035c67e2f289182af95de4cf98e3569e6f`

Algorithm	Hash digest
SHA256	`9f66277180ac28c241ffdaa45bb6c5f568138f98605f375adec1784b1391e57c`
MD5	`2915b7b900e61f18ca7211efb2c8c797`
BLAKE2b-256	`28cbb58b2fa2bb79c1da38f3d621af0f2101671514dec523ef2d56ab7a65d329`

Algorithm	Hash digest
SHA256	`66d241316ef8d50963c313d84aa7b592a546b5318ce2a095145acd837832bcf1`
MD5	`3ace33bc33513bfceabcc7e81e11df8b`
BLAKE2b-256	`3f43e909b0782d1aa1e659f285006db3a880667ab798f0534fbcf1567fb45994`

Algorithm	Hash digest
SHA256	`d3656a4467e1fb71fa974695c652391901a8bc77d89bd06a01b5bf9d2ad31b35`
MD5	`227a2252510703a3bcad7f088a58cc7e`
BLAKE2b-256	`0ee12f6c59e0d5753d0b838a94587727d8fd1e3d04121eea3b309f3307090835`

Algorithm	Hash digest
SHA256	`fa7308dc0567afa31c19d0612347bdeaab02bd0c06984a29926f4a596974ab12`
MD5	`e62a2c8642acf56f6feaffacdf3c4870`
BLAKE2b-256	`bf8efe863ea7048dc26baf2601e119bb8e7a7efaae7a7ea12cc34937b23331b6`

Algorithm	Hash digest
SHA256	`76d4642920845d220872d16c5a76b9fa15dc2bef816a1047a52ccc1729038ddb`
MD5	`ea85a4fbd42b2ce78d014fc40906e29b`
BLAKE2b-256	`1e8b6d5986a72eb1376dd286a2577b0e536e8538727162d52685f12f686614a0`

Algorithm	Hash digest
SHA256	`01ededf14803ebcbf112e77c8586d43ac5f8165c45ce73ed375f053627b74495`
MD5	`12c0963198c971bd8ed78e178bc1a3d6`
BLAKE2b-256	`f0d1bff6ba3f221f331c80f58bd3f97b0df159fb830d60ef1680da26a188418a`

Algorithm	Hash digest
SHA256	`b928ad023b3c6877ee67b06050c35e6fbec667195d8a8dd666fd30f131237a62`
MD5	`03239463671d8a0f125843250d63f15f`
BLAKE2b-256	`587456985db8982348807058ce553f2416ad488965a8af4e7284ce0f27821f7b`

Algorithm	Hash digest
SHA256	`c51bfe37e2c5380473fb70cd77235243cd7615eabf965c96b29e020e033fddc1`
MD5	`69ca5f8f63d477a97f8ff57a9578125b`
BLAKE2b-256	`8905ab457fe536b66e916a780315ffbc947ca56495c26633334e18e921108f88`

Algorithm	Hash digest
SHA256	`cc9b506c6fea7079070597242a77c1ba52eaa359def49e70521d973455323202`
MD5	`a2c0eda35ad96a3b275ef69cd26ce489`
BLAKE2b-256	`c097207ed40da1193620250a29002f99d5004240f52e95e04a255db7655b0b4e`

Algorithm	Hash digest
SHA256	`1aa4329692033b531b6267d1253300b22c0a019b99cca5f82229f8ac2d1bb14f`
MD5	`885421c8aa07d9d65c8a8ce7591e6e77`
BLAKE2b-256	`2007d8ba43d61b2f4f119001abb583963be9aef602c553c1413e3bacfc711b58`

Algorithm	Hash digest
SHA256	`ab0c09f616e5d72271f8d0912e5ed1d63738aebb11a63c39a3872e4f7a0166ca`
MD5	`ffcc21de50c558b4f867c5eb3b80c5b8`
BLAKE2b-256	`368dd0f3e4144d2d927802ef254ed1c71c64098e7ba6bfd25296b1ed4ece4d19`

Algorithm	Hash digest
SHA256	`68f827ed8fe261d68359cb6bac1bb4c4ba98c5d2eb57bcecbf4d035feca47b69`
MD5	`32b3de239d3b893383c254a336d5457f`
BLAKE2b-256	`f344aa2f98361dcb47e49ebd1f11b913fa64e7cc62bb98aee8c70d1a7825926b`

Algorithm	Hash digest
SHA256	`161e07f4c9b7e13d2acb8ffc6fb5e7709d4ee5967d3ce164abe91f3d04638e02`
MD5	`50a43dd9c3f96e1a5f0aa16e85df5f77`
BLAKE2b-256	`e11e7d7d17c041e970ceca6fffa3ba5e89001146a0c1043451bf034cd747e489`

Algorithm	Hash digest
SHA256	`b0504d6c9c4ce7c2bab33519c2a7e854c3af6dc4b8fb16ab3aa940fb62f1f09f`
MD5	`a6c07c563e3628cd10a4396da468057e`
BLAKE2b-256	`2d78cb304ede8cd3bba3506279a20b2c191e1909d2e5ecbbddad1c038852f14a`

Algorithm	Hash digest
SHA256	`d2014420123cc0d6ca4bcc0016270dc1c9e84059d73788ab0ee83b4242c5df5a`
MD5	`2c4aa56c6dfa72ab0b126be8f5a7d215`
BLAKE2b-256	`2c36d9140ffb10b13876d1cd494cf18a58860c869300efc6cb1bcf1a6cfbb82d`

antecedent 0.6.1

Antecedent

Development

Public API

Project links

Key dates

1 maintainer

GitHub Statistics

License

Requires

Classifiers

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

Release history Release notifications | RSS feed