Skip to main content
Glama
vikranthviki

Causal Decision Agent

by vikranthviki

conformal_ite

Read-only

Estimate individual treatment effects with conformal prediction intervals from your data, producing validated evidence tiers for actionable causal decisions.

Instructions

Conformal ITE -- article alias for :func:conformal_cate. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
XYesFeature matrix or covariate DataFrame.
dYesd parameter (str).
yYesOutcome variable column name or outcome array.
detailNoPayload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip.agent
as_handleNoIf true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running.
data_pathYesAbsolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://.
result_idNoOptional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns.
data_columnsNoOptional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
data_sample_nNoOptional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The readOnlyHint annotation already tells the agent this is a safe read operation. The description adds only the phrase 'Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact)', which is a cryptic reference to evidence tiers and does not disclose what happens during execution, how validation works, or what inputs are expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, but brevity here comes from under-specification rather than efficient communication. The first clause restates the tool name, and the validation clause is jargon-heavy without explaining the validation concept.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, a rich output schema, and dozens of sibling tools, this description provides almost no actionable context. An agent cannot determine what conformal_ite does, what data it expects, how it relates to conformal_cate, or when to prefer it over conformal_fair_ite or conformal_synth. The output schema covers return values, but the core operational purpose remains unknown.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the agent already has complete parameter documentation in the schema. The description itself adds no parameter meaning, but per the calibration baseline, when the schema covers all parameters, a score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description says 'Conformal ITE -- article alias for conformal_cate', which identifies it as an alias but never states what the tool computes or what conformal_cate does. 'Conformal ITE' is essentially a rebranding of the name, and the sibling list contains conformal, conformal_fair_ite, and conformal_synth, so the agent cannot distinguish this tool from those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus conformal, conformal_fair_ite, conformal_synth, or any other sibling. The phrase 'article alias' hints at a publication-oriented use case, but it does not state conditions, prerequisites, or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools