WritersLogic Verifiable AI Agent Hub Prove Causality API
The Prove Causality API from WritersLogic Verifiable AI Agent Hub — 1 operation(s) for prove causality.
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curl "https://apis.io/api/v1/apis/writerslogic-com:writerslogic-com-prove-causality-api"
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OpenAPI Specification
openapi: 3.2.0
info:
title: WritersLogic Verifiable AI Agent Hub Prove Causality API
version: 2.0.0
description: Pay-per-call agents settled in USDC on Base via x402. Every response carries a signed provenance envelope.
contact:
name: WritersLogic
url: https://writerslogic.com/agents
email: agents@writerslogic.com
servers:
- url: https://agents.writerslogic.com
tags:
- name: Prove Causality
paths:
/api/v1/prove-causality:
post:
operationId: causalReferee
summary: Causal Claim Referee
description: 'Referees a causal claim against an observational table (JSON rows, CSV text, or a published contingency table with counts) and leads with a verdict, confidence, whether the claim holds, the crude and adjusted effects with bootstrap intervals, and a concrete next step. Underneath: crude and stratified effects, Mantel-Haenszel pooling, Simpson''s-paradox detection, d-separation and backdoor/front-door checks with minimal adjustment sets, g-formula standardization with positivity checks, exact or seeded Monte Carlo permutation tests, and a PC-algorithm check of the DAG against the data. Verdict by code rules; with a caller-supplied DAG no model is called and the result is reproducible. Conclusions hold only under the DAG and no-unmeasured-confounding assumptions, which are listed in the output; this is statistical analysis, not a substitute for a randomized trial.
Price: 1.50 USDC on Base (x402).'
x-x402-price-usdc: '1.50'
x-x402-amount: '1500000'
x-payment-info:
protocols:
- x402
price:
mode: fixed
currency: USD
amount: '1.50'
x-audience: research
x-sample-url: https://agents.writerslogic.com/api/v1/samples/causalReferee
security:
- x402: []
- apiKey: []
requestBody:
required: true
content:
application/json:
schema:
type: object
additionalProperties: false
required:
- treatment
- outcome
- covariates
- claim
properties:
data:
type: array
minItems: 2
maxItems: 20000
items:
type: object
description: Observational table as an array of row objects (at most 20000 rows). Cells are string, number, boolean or null; rows missing a used variable are dropped. Provide this or csv.
csv:
type: string
minLength: 10
maxLength: 3000000
description: 'Alternative to data: CSV text with a header line (comma, semicolon or tab separated; quoted fields allowed; at most 20000 data rows). Numeric cells become numbers.'
countColumn:
type: string
minLength: 1
maxLength: 100
description: Name of a column holding how many times each row repeats, so a published contingency table can be sent as a few aggregate rows (counts must be non-negative integers; the expanded table is capped at the row limit).
treatment:
type: string
minLength: 1
maxLength: 100
description: Column with exactly two distinct values.
outcome:
type: string
minLength: 1
maxLength: 100
description: Binary column (risk difference, risk ratio, odds ratio) or numeric column (mean difference).
covariates:
type: array
maxItems: 12
items:
type: string
minLength: 1
maxLength: 100
description: Candidate adjustment variables (at most 12). Numeric ones with more than `bins` distinct values are cut at deterministic quantile edges; others must have at most 20 levels.
claim:
type: string
minLength: 3
maxLength: 2000
description: The causal claim being refereed, in plain language.
dag:
type: object
additionalProperties: false
required:
- edges
description: 'Your causal DAG (formal input: no LLM is called to build the graph or, by default, the report). Omit to have one proposed by two independent model samples, which is then an assumption.'
properties:
edges:
type: array
maxItems: 200
items:
type: object
additionalProperties: false
required:
- from
- to
properties:
from:
type: string
maxLength: 100
to:
type: string
maxLength: 100
description: Directed edges from cause to effect between the treatment, outcome, covariates and unobserved nodes.
unobserved:
type: array
maxItems: 10
items:
type: string
minLength: 1
maxLength: 100
description: Latent variables that appear in edges (for example an unmeasured confounder).
treatedValue:
anyOf:
- type: string
- type: number
- type: boolean
description: Treatment value that counts as treated; required unless the values are numeric or boolean (then the larger is treated).
successValue:
anyOf:
- type: string
- type: number
- type: boolean
description: Outcome value that counts as success for a binary outcome; same default rule.
outcomeType:
type: string
enum:
- binary
- continuous
description: Force the outcome type; inferred from the number of distinct values otherwise.
claimDirection:
type: string
enum:
- positive
- negative
- nonzero
description: Direction of the claimed effect of treated vs control; default nonzero.
bins:
type: integer
minimum: 2
maximum: 10
description: Quantile bins for numeric covariates, default 4.
alpha:
type: number
minimum: 0.001
maximum: 0.2
description: Significance level for the permutation test and the (1-alpha) bootstrap interval, default 0.05.
seed:
type: integer
minimum: 0
maximum: 4294967295
description: Seed for bootstrap and Monte Carlo; default derived from the input hash.
bootstrapReps:
type: integer
minimum: 100
maximum: 2000
permutationSamples:
type: integer
minimum: 1000
maximum: 200000
description: Used only when the exact enumeration would exceed 1000000 label assignments.
llmReport:
type: boolean
description: 'Ask a model to write the executive report in prose (numbers verified in code, template fallback). Default false: the report is a deterministic template over the computed results.'
descriptions:
type: object
description: Optional one-line description per variable, used only to propose the DAG.
example:
data:
- treatment: A
stone_size: small
success: 1
n: 81
- treatment: A
stone_size: small
success: 0
n: 6
- treatment: A
stone_size: large
success: 1
n: 192
- treatment: A
stone_size: large
success: 0
n: 71
- treatment: B
stone_size: small
success: 1
n: 234
- treatment: B
stone_size: small
success: 0
n: 36
- treatment: B
stone_size: large
success: 1
n: 55
- treatment: B
stone_size: large
success: 0
n: 25
countColumn: n
treatment: treatment
outcome: success
covariates:
- stone_size
treatedValue: B
claim: Treatment B causes a higher recovery rate than treatment A.
claimDirection: positive
dag:
edges:
- from: stone_size
to: treatment
- from: stone_size
to: success
- from: treatment
to: success
responses:
'200':
description: Paid result with provenance
content:
application/json:
schema:
type: object
properties:
data:
type: object
properties:
headline:
type: object
description: 'Verdict-first digest computed in code: verdict, confidence, claimSupported (true, false or null when undecidable), crude and adjusted effect with bootstrap intervals, dagSource, a one-sentence summary and a concrete nextStep.'
deterministic:
type: boolean
description: 'True only for the formal-IR path (caller DAG): no model output can influence the verdict. False when the DAG came from a model or from the outage fallback.'
status:
type: string
enum:
- OK
- BUDGET_EXCEEDED
verdict:
type: string
enum:
- SUPPORTED_UNDER_ASSUMPTIONS
- CONFOUNDED
- SIMPSONS_REVERSAL
- NOT_IDENTIFIABLE
- INSUFFICIENT_DATA
- NOT_SUPPORTED
verdictRule:
type: string
confidence:
type: string
enum:
- low
- medium
- high
reasons:
type: array
items:
type: string
claim:
type: string
claimEvaluation:
type: object
data:
type: object
dag:
type: object
description: The graph used, its source (caller, llm_proposed, default_all_confounders) and whether it is an assumption.
identification:
type: object
description: Minimal backdoor adjustment sets, front-door sets, and variables not to adjust for (mediators, colliders, post-treatment).
effects:
type: object
description: Crude and adjusted risk difference / risk ratio / odds ratio or mean difference with seeded bootstrap intervals.
tests:
type: object
description: Exact or Monte Carlo permutation p-values (within strata for the adjusted test), Fisher exact, Mantel-Haenszel test.
stratification:
type: object
simpson:
type: object
dagCheck:
type: object
description: PC-algorithm skeleton and orientations from the data, and the conflicts with the assumed DAG.
caveats:
type: array
items:
type: string
assumptions:
type: array
items:
type: string
report:
type: object
description: Executive report; source is llm only if every number in it appears in the computed results.
translation:
anyOf:
- type: object
- type: 'null'
audit:
type: object
_provenance:
type: object
description: C2PA-aligned manifest and W3C VC 2.0 (eddsa-jcs-2022) issued by the hub DID; verify at POST /api/v1/verify-provenance
required:
- data
- _provenance
'400':
description: Invalid request (no payment consumed)
content:
application/json:
schema:
type: object
properties:
error:
type: string
code:
type: string
'401':
description: Invalid X-API-KEY
content:
application/json:
schema:
type: object
properties:
error:
type: string
code:
type: string
'402':
description: Payment required or invalid
content:
application/json:
schema:
type: object
description: x402 v2 PaymentRequired (also base64 in the PAYMENT-REQUIRED header)
properties:
x402Version:
type: integer
error:
type: string
code:
type: string
resource:
type: object
accepts:
type: array
items:
type: object
extensions:
type: object
'502':
description: Upstream failure; the payment is released or not settled
content:
application/json:
schema:
type: object
properties:
error:
type: string
code:
type: string
tags:
- Prove Causality
components:
securitySchemes:
x402:
type: apiKey
in: header
name: PAYMENT-SIGNATURE
description: x402 v2 payment payload (base64) sent after a 402 challenge
apiKey:
type: apiKey
in: header
name: X-API-KEY