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Synthetic Control Method (SCM) Skill · brycewang-stanford | DecimalAI

scienceby brycewang-stanford

Synthetic Control Method (SCM) Skill

brycewang-stanford/synthetic-control

Source brycewang-stanford/Auto-Empirical-Research-Skills ↗License NOASSERTION

Econometrics skill for Synthetic Control Method (SCM). Activates when the user asks about: "synthetic control", "SCM", "synthetic counterfactual", "donor pool", "placebo test", "in-space placebo", "in-time placebo", "MSPE ratio", "Abadie Diamond Hainmueller", "augmented synthetic control", "penalized SCM", "synthetic DID", "合成控制", "合成控制法", "捐助池", "安慰剂检验", "合成反事实", "合成DID", "政策评估"

SkillScore100Maintained

Automatic

Research pack

Last verified 8/8/2026

What it changes

Passed 22 of 22 test cases with the skill, and 20 of 22 without it.

Without skill91%

With skill100%

Checks it passed that the model alone missed

identifies no anticipation of treatment before the implementation date
states that pre-treatment RMSPE should be less than 5% of the treated unit pre-treatment outcome mean

2 fixed20 passed either way0 still failing0 broke

Pass-rate lift: +9 pp
Tokens: +239%
Agent turns: 0%
Response time: -12%

Model gemini-3.6-flashMeasured 8/8/2026v122 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-synthetic-control/SKILL.md https://app.decimal.ai/s/brycewang-stanford-synthetic-control/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add brycewang-stanford-synthetic-control, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/brycewang-stanford-synthetic-control/ and pins it in .decimal/skills.lock

decimalai skills add brycewang-stanford-synthetic-control

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-synthetic-control/SKILL.md https://app.decimal.ai/s/brycewang-stanford-synthetic-control/SKILL.md

Raw file: /s/brycewang-stanford-synthetic-control/SKILL.md ↗ · machine-readable summary at /s/brycewang-stanford-synthetic-control.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 2

Prompt

What are two fundamental causal inference assumptions required for classic synthetic control estimation regarding unit interactions and pre-treatment behavior?

What was checked, without and with the skill
Checked	Without skill	With skill
identifies no interference between units or SUTVA	Passed	Passed
identifies no anticipation of treatment before the implementation date	Missed	Passed

case-18 · gemini-3.6-flash · 8/8/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md419 lines2 files

Synthetic Control Method (SCM) Skill

This skill guides complete synthetic control analyses: donor pool construction, weight optimization, gap estimation, placebo-based inference, and extensions including augmented SCM and synthetic DID. Designed for policy evaluation with few treated units.

When to Use Synthetic Control

Situation	Method
Single treated unit, many potential controls	Classic SCM
Few treated units	Multi-unit SCM or Synthetic DID
Treatment at aggregate level (state, country)	Classic SCM
Want DID-like inference with SCM weighting	Synthetic DID (Arkhangelsky et al. 2021)

Key advantage over DID: SCM constructs a data-driven counterfactual rather than assuming parallel trends for all control units equally.

Core Logic

SCM constructs a weighted combination of untreated ("donor") units that best approximates the treated unit's pre-treatment characteristics and outcome trajectory.

Estimand: τ_t = Y₁ₜ − Ŷ₁ₜ^(SC) for post-treatment periods t > T₀

Key Assumptions:

No interference between units (SUTVA)
No anticipation of treatment
Treated unit can be well-approximated by donor pool in pre-treatment period
The data-generating process is stable across pre/post periods

SCM Workflow

Define units and treatment: Identify treated unit, donor pool, treatment date
Select predictors: Choose pre-treatment covariates and outcome lags
Optimize weights: Minimize pre-treatment MSPE between treated and synthetic control
Estimate effects: Gap = treated outcome − synthetic control outcome
Inference: Run placebo tests (in-space and in-time)
Report: Gap plot, placebo plot, MSPE ratios

Code Templates

R — Classic SCM (Synth package)

Track record

Benchmark

22 / 22

test cases passed, vs 20 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/22/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

brycewang-stanford / Auto-Empirical-Research-Skills

skills/67-econfin-workflow-toolkit/synthetic-control/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/2/2026Version v1Bundled 1 file

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Synthetic Control Method (SCM) Skill

When to Use Synthetic Control

Core Logic

SCM Workflow

Code Templates

R — Classic SCM (Synth package)

Track record

Benchmark

Adoption

SkillSafety

Source

Related skills