Causal Decision Agent

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effective_f_test

Read-only

Detect weak instruments using the robust effective F statistic, preventing biased causal estimates in IV analysis. Supports heteroskedasticity-robust and clustered variance options.

Instructions

Olea-Pflueger (2013) robust effective F statistic for weak instruments. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`exog`	No	Included exogenous controls (a constant is added automatically).
`vcov`	No	Variance estimator for the first-stage residuals: - ``'classic'`` -- homoskedastic; F_eff equals first-stage F. - ``'HC0'`` -- White heteroskedasticity-robust. - ``'HC1'`` -- HC0 with small-sample correction ``n/(n-k)``. Ignored when ``cluster`` is given.	HC1
`endog`	Yes	Endogenous regressor (single endogenous variable).
`absorb`	No	High-dimensional fixed effects to partial out of the endogenous regressor, the instruments and the controls before the first stage -- the same residualisation ``sp.iv(absorb=...)`` performs, so the effective F describes the specification actually fitted. The absorbed degrees of freedom are charged to ``df_resid``.
`detail`	No	Payload 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
`cluster`	No	Cluster the first-stage moment variance. ``Omega`` becomes the (multiway, Cameron-Gelbach-Miller) cluster-sum meat with the ``ivreg2`` finite-sample factor ``G_min/(G_min-1) * (n-1)/(n-K)``. This is the right diagnostic whenever the second stage is
`as_handle`	No	If 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_path`	Yes	Absolute 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_id`	No	Optional 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.
`instruments`	Yes	Excluded instruments.
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

C2.9/5.0

Behavior2/5

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

The annotations already signal read-only behavior, so the description carries a lower bar, but it adds essentially no behavioral context. The 'validated evidence tier' sentence is vague and does not explain what the tool returns, how it behaves across vcov choices, or what edge cases matter.

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

Conciseness4/5

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

The two-sentence description is short and front-loaded with the estimator and purpose. The second sentence about 'validated evidence tier' is low-value metadata noise, but it does not meaningfully bloat the description.

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

Completeness3/5

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

For a 12-parameter diagnostic tool, the description is thin: it does not explain the first-stage weak-instrument role, how it relates to sibling weak-IV tools, or what inputs are expected beyond the schema. The presence of an output schema partially compensates for the missing return-value discussion.

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%, and the parameter descriptions for vcov, absorb, cluster, and detail are quite thorough. The tool description itself adds no parameter-level guidance, so the baseline of 3 applies.

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

Purpose4/5

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

The description identifies a specific estimator (Olea-Pflueger 2013 robust effective F) and its domain (weak instruments), which lets an agent distinguish it from generic IV diagnostics. It lacks an explicit verb like 'computes' or 'returns,' and the 'Validation:' sentence is about provenance rather than purpose.

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

Usage Guidelines2/5

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

Aside from the bare phrase 'for weak instruments,' there is no guidance on when to use this tool versus closely related siblings such as zero_first_stage, iv_diag, or weakrobust. No exclusions, prerequisites, or workflow context are provided.

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

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