/empirical-research-methods

name: empirical-research-methods
description: Use this skill for empirical social-science research, applied economics, public policy, education, finance, management, sociology, psychology, epidemiology, or public-health data studies. It routes DID, staggered DID, IV, RDD, PSM/IPW, synthetic control, DML, causal forest, panel regression, event studies, target-trial emulation, TMLE, survival analysis, Table 1, robustness checks, heterogeneity, mechanisms, replication packages, and journal-style empirical paper outputs into MedrixFlow's research quest and experiment_lab workflows.
license: CC BY-SA 4.0 derivative guidance from Awesome-Agent-Skills-for-Empirical-Research

Empirical Research Methods

This skill adapts the workflow ideas from [Awesome Agent Skills for Empirical Research](https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research) into MedrixFlow. It is a routing and integrity layer, not a vendored copy of the whole upstream repository.

Use This Skill For

• empirical papers in economics, policy, education, finance, management, sociology, psychology, epidemiology, public health, or related social-science fields
• causal inference or econometric analysis: DID, staggered DID, IV, RDD, PSM/IPW, entropy balancing, synthetic control, DML, causal forest, target-trial emulation, TMLE, survival, mediation, heterogeneity, mechanisms
• requests such as "run a full empirical analysis", "make Table 1", "event study", "parallel trends", "robustness checks", "replication package", "AER/QJE style table", or "Quarto/Stata/R/Python empirical workflow"
• automatic research quests whose experiment stage depends on a real dataset and a defensible identification strategy

Core Principle

Treat empirical work as a staged design argument, not a single model fit. The agent must first establish the data contract and estimand, then run diagnostics, then estimate, then stress-test, then package results for review.

MedrixFlow Routing

• Use `research_assistant` when the user wants a staged automatic research project, manuscript lifecycle, human gates, reviewer loop, or final bundle.
• Use `academic_research` only for related work, identification precedent, measurement precedent, and citation grounding.
• Use `experiment_lab` for execution on local data. Pass method information in `analysis_type` and `metadata`, including the chosen empirical method, estimand, identifiers, time variables, treatment variables, covariates, fixed effects, cluster level, and robustness plan.
• Use `manuscript_export` only after empirical claims have corresponding result artifacts and citation evidence.

Workflow Contract

1. Intake And Data Contract

Before estimation, identify:

• outcome variable
• treatment or exposure variable
• unit identifier and time variable for panel/event-study designs
• treatment timing for DID/staggered DID
• running variable and cutoff for RDD
• instrument for IV
• matching/IPW covariates for PSM/IPW
• cluster level and fixed effects
• sample restrictions and missing-data policy
• primary estimand: ATE, ATT, LATE, CATE, event-time effect, risk difference, hazard ratio, or predictive metric

If these are missing and cannot be inferred from uploaded data, ask for them or create a blocked research quest entry. Do not silently choose a causal design.

2. Method Selection

Use this decision table:

| User/Data Signal | Default Route | |---|---| | panel policy timing, treated/control groups | DID or staggered DID | | staggered adoption or heterogeneous timing | modern staggered DID; avoid plain TWFE as the only result | | plausible discontinuity threshold | RDD with bandwidth, density, and covariate balance checks | | endogenous treatment plus instrument | IV/2SLS with first-stage F and overidentification checks when applicable | | selection on observables | PSM/IPW/entropy balancing plus balance diagnostics | | single treated unit or few treated units | synthetic control or synthetic DID | | high-dimensional nuisance controls | DML/causal forest with cross-fitting and CATE diagnostics | | public-health cohort or RWE request | target-trial emulation, IPTW/g-formula/TMLE, survival where applicable | | no treatment or causal estimand | descriptive, predictive, or correlational analysis only; label it accordingly |

3. Mandatory Empirical Outputs

For a full empirical analysis, produce an artifact bundle with:

• `experiment_plan.md`: research question, estimand, identification assumptions, variables, sample restrictions, budget, and human approvals
• `methods.md`: data cleaning, model equations, fixed effects, standard errors, diagnostics, and limitations
• `results.md`: main findings with cautious interpretation
• `metrics.json`: structured estimates, standard errors, p-values, fit metrics, diagnostics, and robustness status where available
• `figure_manifest.json`
• tables: Table 1 / balance, main results, robustness, heterogeneity, and mechanisms when applicable
• figures: trend/event-study/RD/balance/coef/spec-curve plots when applicable
• `reproducibility_ledger.json` or equivalent metadata recording dataset paths, seeds, package fallbacks, and skipped checks

4. Identification Gates

Block or downgrade causal language when:

• treatment assignment is not plausibly exogenous and no design handles it
• DID lacks a credible pre-trend or event-study check when pre-period data exists
• IV has weak first-stage evidence
• RDD lacks density/covariate-balance diagnostics
• PSM/IPW lacks post-adjustment balance diagnostics
• DML/CATE work lacks cross-fitting or honest sample splitting where relevant
• the dataset has no declared outcome/treatment for causal claims

When blocked, preserve the exploratory outputs and state what design input is missing. Do not turn correlation into causation.

Experiment Lab Metadata Pattern

When calling `experiment_lab`, include metadata like:

{
  "skill": "empirical-research-methods",
  "empirical_meth