Causal Decision Agent

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survivor_average_causal_effect

Read-only

Calculate sharp bounds on the survivor average causal effect, providing causal estimates for outcomes among survivors despite time-varying confounding.

Instructions

Zhang-Rubin (2003) sharp bounds on the Survivor Average Causal Effect. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact). Assumptions: Sequential exchangeability / no unmeasured confounding at each time point; Positivity: every treatment level is possible given the past; Correct specification of the treatment and/or outcome models. Pre-conditions: Sequentially measured covariates, (time-varying) treatment, and outcome; Models for the treatment process and the outcome (or weights). Failure modes: Stabilized weights have extreme values (positivity near-violation) -> Truncate weights, simplify the treatment model, or use a doubly-robust estimator (TMLE). Alternatives: sp.tmle, sp.g_computation, sp.ipw. Typical minimum N: 300.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable column name or outcome array.
`seed`	No	Random seed for reproducible stochastic steps.
`alpha`	No	Significance level for confidence intervals and tests.
`treat`	Yes	Treatment indicator or first-treatment-period column.
`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.
`survival`	Yes	survival parameter (str).
`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.
`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

A4.1/5.0

Behavior4/5

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

With readOnlyHint already set, the description adds valuable behavioral context: failure modes (stabilized weights with extreme values), suggested remedies (truncate weights, simplify models, use TMLE), validation tiers, and typical minimum N. No contradiction with 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 structured with clear labels (Validation, Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N) and front-loads the core method and estimand. It is information-dense but each section earns its place.

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 causal-inference tool, the description covers assumptions, preconditions, failure modes, alternatives, and minimum sample size. It does not explain return values, but an output schema is available, so that gap is acceptable.

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 baseline is 3. The description does not add parameter-specific meanings (e.g., what 'survival' or 'treat' should contain), but it does provide background context about time-varying treatments and weights that helps interpret parameters indirectly.

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

Purpose5/5

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

The description names a specific method (

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?

The description provides Assumptions, Pre-conditions, and explicitly lists Alternatives (sp.tmle, sp.g_computation, sp.ipw). It gives clear context for when this tool is appropriate (SACE under sequential exchangeability) but does not explicitly state when to choose it over the alternatives or when not to use it.

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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