Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 22:31 UTC

py-why / dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

PythonMIT★ 8,260 stars⑂ 1,047 forkssince May 2018View on GitHub ↗

KindLibraryhow this is determined

#causal-inference #machine-learning #graphical-models #bayesian-networks #data-science #python3 #causality #causal-models #treatment-effects #do-calculus #causal-machine-learning

py-why/dowhy holds a health index of 93 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (89/100) and lowest on Security (67/100). It was last updated today. 2 contributors account for most of its recent work.

overall / 100

Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met

Excellent80-92Strong across the board; minor gaps

Good65-79Healthy; gaps are limited and manageable

Moderate50-64Acceptable with notable gaps; review recommended

Weak35-49Material weaknesses across several areas

At Risk20-34Significant weaknesses; adoption warrants caution

Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 79 is calibrated to 93 on the published index scale (record calibration 2026-08-02).

Ownership

PyWhyOrganization

1,055 followers14 public repossince Mar 2022

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	dowhy	0.14	-	18	277 days ago	causality machine-learning causal-inference statistics graphical-model

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

73Good · 21% of overall

Development activity

81Excellent

How it's scored

36/36	Push recency — last push 0 days ago
17.3/36	Commit cadence — 25/52 weeks with commits
18/18	Commit volume — 135 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	135
human_commit_share	0.36
days_since_last_push	0
active_weeks_last_year	25

Release discipline

62Moderate

How it's scored

27/27	Ships releases — 18 releases published
16.2/36	Release recency — latest release 277 days ago
12.6/27	Release cadence — a release every ~134.3 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	18
latest_release_tag	v0.14
releases_from_tags	no
days_since_latest_release	277
mean_days_between_releases	134.3

Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

84Excellent · 17% of overall

Popularity & adoption

97Exceptional

How it's scored

60/60	Stars — 8,260 stars
25/25	Forks — 1,047 forks
11.8/15	Watchers — 136 watchers

Inputs used

forks	1,047
stars	8,260
watchers	136
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

70Good

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (MIT)
18/18	CONTRIBUTING guide
0/13.5	Code of conduct
0/7.2	Issue template
0/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	0
has_contributing	yes
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	—
has_pull_request_template	no

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

80Excellent · 23% of overall

Maintainer resilience (bus factor)

64Moderate

How it's scored

25.2/54	Bus factor — 2 contributor(s) cover half of all commits
14.9/22.5	Commit distribution — top contributor authored 34% of commits
13.5/13.5	Contributor breadth — 96 contributors
10/10	OpenSSF Scorecard: Contributors — project has 12 contributing companies or organizations

Inputs used

bus_factor	2
contributors_sampled	96
top_contributor_share	0.336

Issue & PR responsiveness

83Excellent

How it's scored

32.8/42	Issue resolution — 78% of issues closed
24.4/30	PR acceptance — 814/999 decided PRs merged
0/13	Newcomer PR acceptance — no first-time contributor's PR decided in 30d
15/15	OpenSSF Scorecard: Code-Review — all changesets reviewed

Inputs used

merged_prs	814
open_issues	133
closed_issues	475
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	3
prs_decided_30d	3
issue_closed_ratio	0.781
closed_unmerged_prs	185
first_time_authors_30d	0
first_time_prs_merged_30d	0
first_time_prs_decided_30d	0

Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

86Excellent

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain — verified-domain status not read for this organization
21.7/25	Owner reach — 1,055 followers of py-why
17.4/25	Track record — 14 public repos, account ~4 yr old

Inputs used

followers	1,055
owner_type	Organization
is_verified	—
owner_login	py-why
public_repos	14
account_age_days	1,617

Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

91Excellent

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
26/35	Publish recency — latest publish 277 days ago
20/20	Version history — 18 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	dowhy
ecosystems	pypi
any_deprecated	no
min_days_since_publish	277

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall

Engineering practices

82Excellent

How it's scored

24/24	CI workflows — 12 workflow(s)
24/24	Tests present
16/16	Linter config — .flake8
0/9.6	Pre-commit hooks
0/6.4	.editorconfig
18/20	OpenSSF Scorecard: CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	no

Documentation

100Exceptional

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://www.pywhy.org/dowhy
10/10	Repository description
10/10	Topics — 11 topics
10/10	Wiki

Inputs used

topics	causal-inference, machine-learning, graphical-models, bayesian-networks, data-science, python3, causality, causal-models, treatment-effects, do-calculus, causal-machine-learning
has_wiki	yes
homepage	https://www.pywhy.org/dowhy
docs_site	https://www.pywhy.org/dowhy
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

67Good · 16% of overall

Security posture

59Moderate

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
2.2/2.5	CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5	Code-Review — all changesets reviewed
2.5/2.5	Contributors — project has 12 contributing companies or organizations
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
1/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
0/5	SAST — SAST tool is not run on all commits -- score normalized to 0
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5	Vulnerabilities — 89 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	5.9

Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	42
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:dowhy@0.14 runtime dependency closure — what installing the published package pulls in — 42 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

81Excellent · 4% of overall

Agent context & guidance

85Excellent

How it's scored

45/45	Agent instructions — AGENTS.md
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 32 of 36 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.889
agent_instruction_files	AGENTS.md
agent_instruction_max_bytes	8,083

Verify loop (build / test / typecheck)

81Excellent

How it's scored

18/18	One-command bootstrap — docs/source/Makefile
22/22	Automated tests
11/11	Lint / format config — .flake8
0/11	Static type checking
10/10	Reproducible environment — Dockerfile, lockfile
10/10	Demonstrated agent practice — 42 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 19 of the last 100 commits are automated dependency updates
2/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2

Inputs used

has_nix	no
has_tests	yes
lockfiles	package-lock.json, poetry.lock
has_dockerfile	yes
typed_language	no
bootstrap_files	docs/source/Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	—
agent_commit_share	0.42
toolchain_manifests	—
dependency_bot_commit_share	0.19

Code legibility for models

55Moderate

How it's scored

0/45	Type-checkable code — Python without a type-check config
54.8/55	Manageable file sizes — 1/268 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	60,243
source_files_sampled	268
oversized_source_files	1

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — notebooks

Inputs used

example_dirs	notebooks
has_mcp_signal	no
api_schema_files	—
interfaces_expected_of	—

Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

8,260GitHub stars

96contributors

135commits, last 12 months

0days since last push

18releases

2bus factor

133open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 1,047 ⇿

0Stars

1,047Forks

18Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

Major 0Minor 0Patch 5

Each point covers 8 days.

OpenSSF Scorecard 5.9 / 10

5.9aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-12 22:30 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
9	CI-Tests	29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
10	Code-Review	all changesets reviewed
10	Contributors	project has 12 contributing companies or organizations
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
2	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 2
0	SAST	SAST tool is not run on all commits -- score normalized to 0
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
0	Vulnerabilities	89 existing vulnerabilities detected

Direct dependencies 17

Registry	Package	Version constraint	Manifest
PyPI	cython	`>=3.0`	pyproject.toml
PyPI	scipy	`—`	pyproject.toml
PyPI	statsmodels	`>=0.14`	pyproject.toml
PyPI	numpy	`>2.0`	pyproject.toml
PyPI	pandas	`>1.0`	pyproject.toml
PyPI	networkx	`—`	pyproject.toml
PyPI	sympy	`>=1.10.1`	pyproject.toml
PyPI	scikit-learn	`>1.0`	pyproject.toml
PyPI	pydot	`^1.4.2`	pyproject.toml
PyPI	joblib	`>=1.1.0`	pyproject.toml
PyPI	pygraphviz	`>=1.9`	pyproject.toml
PyPI	econml	`>=0.16`	pyproject.toml
PyPI	tqdm	`>=4.64.0`	pyproject.toml
PyPI	causal-learn	`>=0.1.4.4`	pyproject.toml
PyPI	numba	`>=0.59`	pyproject.toml
PyPI	matplotlib	`>=3.5.3`	pyproject.toml
PyPI	cvxpy	`^1.7.1`	pyproject.toml

All dependencies 265

Full resolved dependency set from the GitHub dependency graph: 17 direct and 248 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	causal-learn	`0.1.4.4`	direct
PyPI	cvxpy	`1.7.1`	direct
PyPI	cython	`3.1.2`	direct
PyPI	econml	`0.16.0`	direct
PyPI	joblib	`1.5.1`	direct
PyPI	matplotlib	`3.9.4`	direct
PyPI	networkx	`3.2.1`	direct
PyPI	numba	`0.60.0`	direct
PyPI	numpy	`2.0.2`	direct
PyPI	pandas	`2.3.1`	direct
PyPI	pydot	`1.4.2`	direct
PyPI	pygraphviz	`1.11`	direct
PyPI	scikit-learn	`1.6.1`	direct
PyPI	scipy	`1.13.1`	direct
PyPI	statsmodels	`0.14.5`	direct
PyPI	sympy	`1.14.0`	direct
PyPI	tqdm	`4.67.1`	direct
npm	@asamuzakjp/css-color	`5.0.1`	indirect
npm	@asamuzakjp/dom-selector	`7.0.3`	indirect
npm	@asamuzakjp/nwsapi	`2.3.9`	indirect
npm	@bramus/specificity	`2.4.2`	indirect
npm	@csstools/color-helpers	`6.0.2`	indirect
npm	@csstools/css-calc	`3.1.1`	indirect
npm	@csstools/css-color-parser	`4.0.2`	indirect
npm	@csstools/css-parser-algorithms	`4.0.0`	indirect
npm	@csstools/css-syntax-patches-for-csstree	`1.1.1`	indirect
npm	@csstools/css-tokenizer	`4.0.0`	indirect
npm	@exodus/bytes	`1.15.0`	indirect
npm	bidi-js	`1.0.3`	indirect
npm	css-tree	`3.2.1`	indirect
npm	data-urls	`7.0.0`	indirect
npm	decimal.js	`10.6.0`	indirect
npm	entities	`6.0.1`	indirect
npm	html-encoding-sniffer	`6.0.0`	indirect
npm	is-potential-custom-element-name	`1.0.1`	indirect
npm	jsdom	`29.0.0`	indirect
npm	lru-cache	`11.2.7`	indirect
npm	mdn-data	`2.27.1`	indirect
npm	parse5	`8.0.0`	indirect
npm	punycode	`2.3.1`	indirect
npm	require-from-string	`2.0.2`	indirect
npm	saxes	`6.0.0`	indirect
npm	source-map-js	`1.2.1`	indirect
npm	symbol-tree	`3.2.4`	indirect
npm	tldts	`7.0.26`	indirect
npm	tldts-core	`7.0.26`	indirect
npm	tough-cookie	`6.0.1`	indirect
npm	tr46	`6.0.0`	indirect
npm	undici	`7.28.0`	indirect
npm	w3c-xmlserializer	`5.0.0`	indirect
npm	webidl-conversions	`8.0.1`	indirect
npm	whatwg-mimetype	`5.0.0`	indirect
npm	whatwg-url	`16.0.1`	indirect
npm	xml-name-validator	`5.0.0`	indirect
npm	xmlchars	`2.2.0`	indirect
PyPI	absl-py	`2.3.1`	indirect
PyPI	accessible-pygments	`0.0.5`	indirect
PyPI	aiohappyeyeballs	`2.6.1`	indirect
PyPI	aiohttp	`3.13.4`	indirect
PyPI	aiosignal	`1.4.0`	indirect
PyPI	alabaster	`0.7.16`	indirect
PyPI	anyio	`4.9.0`	indirect
PyPI	appnope	`0.1.4`	indirect
PyPI	argon2-cffi	`25.1.0`	indirect
PyPI	argon2-cffi-bindings	`21.2.0`	indirect
PyPI	arrow	`1.3.0`	indirect
PyPI	asttokens	`3.0.0`	indirect
PyPI	async-lru	`2.0.5`	indirect
PyPI	async-timeout	`5.0.1`	indirect
PyPI	attrs	`25.3.0`	indirect
PyPI	babel	`2.17.0`	indirect
PyPI	beautifulsoup4	`4.13.4`	indirect
PyPI	black	`25.1.0`	indirect
PyPI	bleach	`6.2.0`	indirect
PyPI	cdt	`0.6.0`	indirect
PyPI	certifi	`2025.7.9`	indirect
PyPI	cffi	`1.17.1`	indirect
PyPI	charset-normalizer	`3.4.2`	indirect
PyPI	clarabel	`0.11.1`	indirect
PyPI	click	`8.1.8`	indirect
PyPI	cloudpickle	`3.1.1`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	comm	`0.2.2`	indirect
PyPI	contourpy	`1.3.0`	indirect
PyPI	coverage	`7.9.2`	indirect
PyPI	cycler	`0.12.1`	indirect
PyPI	debugpy	`1.8.14`	indirect
PyPI	decorator	`5.2.1`	indirect
PyPI	defusedxml	`0.7.1`	indirect
PyPI	docutils	`0.20.1`	indirect
PyPI	exceptiongroup	`1.3.0`	indirect
PyPI	executing	`2.2.0`	indirect
PyPI	fastjsonschema	`2.21.1`	indirect
PyPI	filelock	`3.18.0`	indirect
PyPI	flake8	`7.3.0`	indirect
PyPI	flaky	`3.8.1`	indirect
PyPI	fonttools	`4.60.2`	indirect
PyPI	fqdn	`1.5.1`	indirect
PyPI	frozenlist	`1.7.0`	indirect
PyPI	fsspec	`2025.5.1`	indirect
PyPI	gputil	`1.4.0`	indirect
PyPI	graphviz	`0.21`	indirect
PyPI	h11	`0.16.0`	indirect
PyPI	h5py	`3.14.0`	indirect
PyPI	httpcore	`1.0.9`	indirect
PyPI	httpx	`0.28.1`	indirect
PyPI	idna	`3.15`	indirect
PyPI	imagesize	`1.4.1`	indirect
PyPI	importlib-metadata	`8.7.0`	indirect
PyPI	importlib-resources	`6.5.2`	indirect
PyPI	iniconfig	`2.1.0`	indirect
PyPI	ipykernel	`6.29.5`	indirect
PyPI	ipython	`8.18.1`	indirect
PyPI	ipython-pygments-lexers	`1.1.1`	indirect
PyPI	ipywidgets	`8.1.7`	indirect
PyPI	isoduration	`20.11.0`	indirect
PyPI	isort	`6.0.1`	indirect
PyPI	jedi	`0.19.2`	indirect
PyPI	jinja2	`3.1.6`	indirect
PyPI	json5	`0.12.0`	indirect
PyPI	jsonpointer	`3.0.0`	indirect
PyPI	jsonschema	`4.24.0`	indirect
PyPI	jsonschema-specifications	`2025.4.1`	indirect
PyPI	jupyter	`1.1.1`	indirect
PyPI	jupyter-client	`8.6.3`	indirect
PyPI	jupyter-console	`6.6.3`	indirect
PyPI	jupyter-core	`5.8.1`	indirect
PyPI	jupyter-events	`0.12.0`	indirect
PyPI	jupyter-lsp	`2.2.5`	indirect
PyPI	jupyter-server	`2.18.0`	indirect
PyPI	jupyter-server-terminals	`0.5.3`	indirect
PyPI	jupyterlab	`4.5.10`	indirect
PyPI	jupyterlab-pygments	`0.3.0`	indirect
PyPI	jupyterlab-server	`2.28.0`	indirect
PyPI	jupyterlab-widgets	`3.0.15`	indirect
PyPI	keras	`3.10.0`	indirect
PyPI	kiwisolver	`1.4.7`	indirect
PyPI	lightgbm	`4.6.0`	indirect
PyPI	lightning-utilities	`0.14.3`	indirect
PyPI	llvmlite	`0.43.0`	indirect
PyPI	markdown-it-py	`3.0.0`	indirect
PyPI	markupsafe	`3.0.2`	indirect
PyPI	matplotlib-inline	`0.1.7`	indirect
PyPI	mccabe	`0.7.0`	indirect
PyPI	mdurl	`0.1.2`	indirect
PyPI	mistune	`3.3.0`	indirect
PyPI	ml-dtypes	`0.4.1`	indirect
PyPI	momentchi2	`0.1.8`	indirect
PyPI	mpmath	`1.3.0`	indirect
PyPI	multidict	`6.6.3`	indirect
PyPI	mypy	`1.16.1`	indirect
PyPI	mypy-extensions	`1.1.0`	indirect
PyPI	namex	`0.1.0`	indirect
PyPI	nbclient	`0.10.2`	indirect
PyPI	nbconvert	`7.17.1`	indirect
PyPI	nbformat	`5.10.4`	indirect
PyPI	nbsphinx	`0.9.7`	indirect
PyPI	nest-asyncio	`1.6.0`	indirect
PyPI	notebook	`7.5.6`	indirect
PyPI	notebook-shim	`0.2.4`	indirect
PyPI	nvidia-cublas-cu12	`12.1.3.1`	indirect
PyPI	nvidia-cuda-cupti-cu12	`12.1.105`	indirect
PyPI	nvidia-cuda-nvrtc-cu12	`12.1.105`	indirect
PyPI	nvidia-cuda-runtime-cu12	`12.1.105`	indirect
PyPI	nvidia-cudnn-cu12	`9.1.0.70`	indirect
PyPI	nvidia-cufft-cu12	`11.0.2.54`	indirect
PyPI	nvidia-cufile-cu12	`1.11.1.6`	indirect
PyPI	nvidia-curand-cu12	`10.3.2.106`	indirect
PyPI	nvidia-cusolver-cu12	`11.4.5.107`	indirect
PyPI	nvidia-cusparse-cu12	`12.1.0.106`	indirect
PyPI	nvidia-cusparselt-cu12	`0.6.3`	indirect
PyPI	nvidia-nccl-cu12	`2.20.5`	indirect
PyPI	nvidia-nvjitlink-cu12	`12.6.85`	indirect
PyPI	nvidia-nvtx-cu12	`12.1.105`	indirect
PyPI	optree	`0.16.0`	indirect
PyPI	osqp	`1.0.4`	indirect
PyPI	overrides	`7.7.0`	indirect
PyPI	packaging	`25.0`	indirect
PyPI	pandocfilters	`1.5.1`	indirect
PyPI	parso	`0.8.4`	indirect
PyPI	pastel	`0.2.1`	indirect
PyPI	pathspec	`0.12.1`	indirect
PyPI	patsy	`1.0.1`	indirect
PyPI	pexpect	`4.9.0`	indirect
PyPI	pillow	`11.3.0`	indirect
PyPI	platformdirs	`4.3.8`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	poethepoet	`0.36.0`	indirect
PyPI	prometheus-client	`0.22.1`	indirect
PyPI	prompt-toolkit	`3.0.51`	indirect
PyPI	propcache	`0.3.2`	indirect
PyPI	psutil	`7.0.0`	indirect
PyPI	ptyprocess	`0.7.0`	indirect
PyPI	pure-eval	`0.2.3`	indirect
PyPI	pycodestyle	`2.14.0`	indirect
PyPI	pycparser	`2.22`	indirect
PyPI	pydata-sphinx-theme	`0.16.1`	indirect
PyPI	pyflakes	`3.4.0`	indirect
PyPI	pygments	`2.20.0`	indirect
PyPI	pyparsing	`3.2.3`	indirect
PyPI	pytest	`8.4.1`	indirect
PyPI	pytest-cov	`6.2.1`	indirect
PyPI	pytest-split	`0.10.0`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	python-json-logger	`3.3.0`	indirect
PyPI	pytorch-lightning	`1.9.5`	indirect
PyPI	pytz	`2025.2`	indirect
PyPI	pywin32	`310`	indirect
PyPI	pywinpty	`2.0.15`	indirect
PyPI	pyyaml	`6.0.2`	indirect
PyPI	pyzmq	`27.0.0`	indirect
PyPI	referencing	`0.36.2`	indirect
PyPI	requests	`2.32.4`	indirect
PyPI	rfc3339-validator	`0.1.4`	indirect
PyPI	rfc3986-validator	`0.1.1`	indirect
PyPI	rich	`14.0.0`	indirect
PyPI	rpds-py	`0.26.0`	indirect
PyPI	scs	`3.2.7.post2`	indirect
PyPI	seaborn	`0.13.2`	indirect
PyPI	send2trash	`1.8.3`	indirect
PyPI	setuptools	`80.9.0`	indirect
PyPI	shap	`0.48.0`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	skrebate	`0.62`	indirect
PyPI	slicer	`0.0.8`	indirect
PyPI	sniffio	`1.3.1`	indirect
PyPI	snowballstemmer	`3.0.1`	indirect
PyPI	soupsieve	`2.8.4`	indirect
PyPI	sparse	`0.15.5`	indirect
PyPI	sphinx	`7.4.7`	indirect
PyPI	sphinx-copybutton	`0.5.0`	indirect
PyPI	sphinx-design	`0.6.1`	indirect
PyPI	sphinx-rtd-theme	`3.0.2`	indirect
PyPI	sphinxcontrib-applehelp	`2.0.0`	indirect
PyPI	sphinxcontrib-devhelp	`2.0.0`	indirect
PyPI	sphinxcontrib-htmlhelp	`2.1.0`	indirect
PyPI	sphinxcontrib-jquery	`4.1`	indirect
PyPI	sphinxcontrib-jsmath	`1.0.1`	indirect
PyPI	sphinxcontrib-qthelp	`2.0.0`	indirect
PyPI	sphinxcontrib-serializinghtml	`2.0.0`	indirect
PyPI	stack-data	`0.6.3`	indirect
PyPI	terminado	`0.18.1`	indirect
PyPI	threadpoolctl	`3.6.0`	indirect
PyPI	tinycss2	`1.4.0`	indirect
PyPI	tokenize-rt	`6.2.0`	indirect
PyPI	tomli	`2.2.1`	indirect
PyPI	torch	`2.4.1`	indirect
PyPI	torchmetrics	`1.7.4`	indirect
PyPI	torchvision	`0.19.1`	indirect
PyPI	tornado	`6.5.7`	indirect
PyPI	traitlets	`5.14.3`	indirect
PyPI	triton	`3.0.0`	indirect
PyPI	types-python-dateutil	`2.9.0.20250708`	indirect
PyPI	typing-extensions	`4.14.1`	indirect
PyPI	tzdata	`2025.2`	indirect
PyPI	uri-template	`1.3.0`	indirect
PyPI	urllib3	`2.6.3`	indirect
PyPI	wcwidth	`0.2.13`	indirect
PyPI	webcolors	`24.11.1`	indirect
PyPI	webencodings	`0.5.1`	indirect
PyPI	websocket-client	`1.8.0`	indirect
PyPI	widgetsnbextension	`4.0.14`	indirect
PyPI	xgboost	`2.1.4`	indirect
PyPI	yarl	`1.20.1`	indirect
PyPI	zipp	`3.23.0`	indirect

Dependency advisories 0

Installing pypi:dowhy@0.14 pulls in 42 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.

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