simulation-study

/simulation-study — Monte Carlo Simulation Study

Design and run a Monte Carlo experiment that characterizes an estimator's finite-sample behavior, then review it for the bugs that quietly invalidate simulation evidence.

Input: $ARGUMENTS — a description of the estimator(s) and DGP to study (e.g., "compare 2SLS vs LIML under weak instruments with heteroskedasticity"), or a pointer to an existing script/paper whose simulation you want to reproduce or extend.

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Constraints

• Follow .claude/rules/simulation-conventions.md — the simulation contract (DGP, truth, estimand, MCSE, assumption regime) is non-negotiable.
• Declare the assumption regime in the script header and respect the firewall — an out-of-assumption run never supports a within-assumption claim (simulation-conventions.md §2).
• Follow .claude/rules/r-code-conventions.md for general R standards (header, library() at top, relative paths, numerical discipline).
• Save the script to scripts/R/ with a numbered, descriptive name (e.g., scripts/R/sim_2sls_vs_liml.R).
• Save outputs (per-rep raw tibble, summary table, figures) to scripts/R/_outputs/.
• saveRDS() the per-replication raw results, not just the summary — re-aggregation and the review pass need them.
• Run the sim-reviewer agent on the generated script before presenting results, then address Critical/High findings.

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Workflow Phases

Phase 0: Pre-Flight Report

Before writing any code, produce a Pre-Flight Report showing you have pinned down the experiment. This prevents the most common failure mode — a beautiful results table built on a mismatched estimand or a coverage-against-the-estimate bug.

## Pre-Flight Report — Simulation Design

**Research question:** [what finite-sample property is being demonstrated]
**Target estimand:** [ATT / ATE / coefficient θ — and how its TRUE value is computed from the DGP params]
**Maintained assumptions:** [the FULL list the estimator(s) under study require — A1 … An, every one]
**Regime:** [IN-ASSUMPTION — all hold | OUT-OF-ASSUMPTION — relaxes A[k] only, severity grid {…}, targeting pseudo-estimand …]
**Verification:** [per assumption, the checkable property of the DGP that establishes it — by construction or by an assertion]
**DGP:** [structure + the parameters that define it; what is held fixed vs. varied]
**Estimator grid:** [list each estimator + which estimand it targets + how it returns est/se/CI]
**Design grid:** [sample sizes, parameter values, scenarios to sweep]
**Replications R:** [value] → implied MCSE on coverage ≈ sqrt(0.95·0.05/R) = [value]
**Metrics:** bias, empirical SE, RMSE, coverage, size/power — each with MCSE
**Conventions read:** simulation-conventions.md, r-code-conventions.md