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dfl_decompose

Read-only

Decompose group outcome gaps at a chosen statistic via DFL propensity-score reweighting, separating composition from structure effects. Provides diagnostics and certified parity evidence.

Instructions

DFL (1996) reweighting decomposition at a chosen distributional statistic. Validation: certified parity evidence. Assumptions: DiNardo-Fortin-Lemieux reweighting: ignorable group assignment given covariates; Propensity-score model is correctly specified for the reweighting kernel; Common support across groups (no extrapolation beyond observed covariate range). Pre-conditions: Binary group indicator with sufficient overlap on covariates; Outcome distribution to decompose is continuous (typically log-wage). Failure modes: Extreme propensity-score weights inflate variance -> Trim or stabilize weights, or restrict to the common-support region. Alternatives: sp.ffl_decompose, sp.oaxaca, sp.machado_mata. Typical minimum N: 500.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Primary running variable, regressor, or feature input for this estimator.
`y`	Yes	Outcome variable column name or outcome array.
`tau`	No	Quantile level or target treatment-effect index.
`seed`	No	Random seed for reproducible stochastic steps.
`stat`	No	stat parameter (str).	mean
`trim`	No	trim parameter (float).
`alpha`	No	Significance level for confidence intervals and tests.
`group`	Yes	Group or cohort identifier.
`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
`n_boot`	No	Number of bootstrap replications.
`weights`	No	Observation weights.
`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://.
`inference`	No	inference parameter (str).	analytical
`reference`	No	- 0: reweight Group B to look like A's X (default). The counterfactual is F_{Y<1\|0>} -- A's X distribution with B's outcome structure. - 1: reweight Group A to look like B's X. The counterfactual is F_{Y<0\|1>} -- B's X distribution with A's outcome structure. .. warning:: ``reference`` has different economic semantics across method families. In DFL, ``reference=0`` yields cf = A's X, B's beta (reweighting approach). In ``machado_mata`` / ``melly`` / ``cfm``, ``reference=0`` yields cf = A's beta, B's X (coefficient-substitution approach). These are opposite counterfactual constructions. Within each method labels are internally consistent (DFL structure = A - cf; MM composition = A - cf). When comparing estimates across methods, read the per-method docstrings carefully.
`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.
`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.
`quantile_grid`	No	If provided, also compute quantile-process decomposition on this grid.
`stat_convention`	No	Weighted variance / quantile definition for the reweighted counterfactual (see ``_common.statistic_value``). ``'hmisc'`` reproduces ``ddecompose::dfl_decompose``, which uses ``Hmisc::wtd.var`` and ``Hmisc::wtd.quantile``; the reweighting itself is identical under both. ``stat='gini'`` is the exact plug-in Gini either way; ``ddecompose`` integrates the Lorenz curve numerically and differs from it in the fourth significant digit.	statspai

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

A3.9/5.0

Behavior4/5

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

The description discloses assumptions (ignorability, propensity-score specification, common support), failure modes, and recommended remedies, which go beyond the readOnlyHint annotation. It also mentions validation and typical minimum N, adding useful behavioral context without contradicting the annotations.

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 description is front-loaded with the core purpose and then organized into labeled sections (Validation, Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N). It is somewhat long but every section contributes actionable information for tool selection and correct invocation.

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

Completeness4/5

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

For a complex decomposition tool with 20 parameters, the description covers decisive context: assumptions, preconditions, failure modes, alternatives, and minimum sample size. The output schema handles return-value documentation, so the absence of output details is not a gap. It is complete enough for an agent to decide whether and how to use the tool.

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?

Since schema description coverage is 100%, the baseline is 3. The description only loosely references the distributional statistic but does not add parameter-level meaning; the schema already fully explains parameters like stat, reference, and trim.

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 clearly states it performs DFL (1996) reweighting decomposition at a chosen distributional statistic, which identifies the method and resource. It names alternatives (sp.ffl_decompose, sp.oaxaca, sp.machado_mata) but does not explicitly differentiate its scope from them, leaving some ambiguity for agents unfamiliar with these methods.

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

Usage Guidelines4/5

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

It provides concrete pre-conditions (binary group indicator, continuous outcome, common support) and failure modes (extreme propensity-score weights) that help an agent decide when this tool is appropriate. However, it does not explicitly state when to choose DFL over the listed alternatives, so the guidance is context-rich but lacks explicit exclusion criteria.

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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