Synthetic control design worksheet

Credibility depends on pre-fit and suitable donor selection. Record design decisions here.

When to use this worksheet and what completion involves Fictional completed example

EvalCompass Worksheet

Synthetic control design worksheet

Method: Synthetic Control Method

Credibility depends on pre-fit and suitable donor selection. Record design decisions here.

Basic information

Enter evaluation basics

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Policy/intervention name: City A consultation scheme
Analyst: Analyst A (fictional)
Data sources: Consistently defined city-level use indicators (fictional)
Creation date: July 1, 2026 (fictional)

Reason for this entry: Compare definitions across cities.

Policy/intervention name

Analyst

Data sources

Creation date

Writing tips and explanation for this section

About this section

Enter evaluation basics

Compare definitions across cities.

View the method explanation

Writing tips

Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.

Read the full fictional example

Define treated units and donor pool

Enter selection rationale

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Scroll horizontally to view the table

Unit name	Treated / donor	Inclusion/exclusion reasons
City A	Intervention	Scheme introduction target
City B	Candidate donor	No scheme; spillovers to be checked
City C	Candidate donor	No scheme; spillovers to be checked

Reason for this entry: Candidates are not automatically eligible.

Scroll horizontally to view the table

Unit name	Treated / donor	Inclusion/exclusion reasons

Writing tips and explanation for this section

About this section

Enter selection rationale

Candidates are not automatically eligible.

View the method explanation

Writing tips

Keep the information in each row aligned. Scroll wide tables horizontally to see every column.

Read the full fictional example

Variables for synthesis

Enter weighting variables and rationale

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Outcome name: Uses per 100 target people
Pre-outcome timepoints used: Illustration uses pre 1, pre 2 and pre 3
Additional predictive covariates: Consider population composition in actual analysis
Theoretical selection rationale: Align pre-factors affecting scheme use

Reason for this entry: Do not finalize variables from a short illustration alone.

Outcome name

Pre-outcome timepoints used

Additional predictive covariates

Theoretical selection rationale

Writing tips and explanation for this section

About this section

Enter weighting variables and rationale

Do not finalize variables from a short illustration alone.

View the method explanation

Writing tips

Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.

Read the full fictional example

Assess pre-fit

Enter pre-fit results

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

Pre-MSPE: mean squared prediction error: 1/3: fictional-table calculation
Visual fit: good / acceptable / insufficient: Undecided; 3 illustrative timepoints cannot establish sufficiency
Response to inadequate fit: Add periods and review donors/predictors

Reason for this entry: Small error alone does not establish validity.

Pre-MSPE: mean squared prediction error

Visual fit: good / acceptable / insufficient

Response to inadequate fit

Writing tips and explanation for this section

About this section

Enter pre-fit results

Small error alone does not establish validity.

View the method explanation

Writing tips

Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.

Read the full fictional example

Design checklist

Completed example for this section (fictional)

This fictional example is for explanation. Adapt it to your own program. Your entries will not change.

☐ Not checked: Assessed construction/fit feasibility from pre-series length and variation
☐ Not checked: Excluded potentially exposed donors
☑ Checked: No post-data in weight optimization
☑ Checked: Checked pre-fit with MSPE and series plots
☐ Not checked: Conducted identical placebo analyses for all donors
☐ Not checked: Reported post/pre MSPE ratios
☑ Checked: Included donor weights in report

Reason for this entry: Weights 0.5/0.5 and pre-errors are illustrated; actual duration needs and placebos remain unconfirmed.

Assessed construction/fit feasibility from pre-series length and variation
Excluded potentially exposed donors
No post-data in weight optimization
Checked pre-fit with MSPE and series plots
Conducted identical placebo analyses for all donors
Reported post/pre MSPE ratios
Included donor weights in report

Writing tips and explanation for this section

About this section

Weights 0.5/0.5 and pre-errors are illustrated; actual duration needs and placebos remain unconfirmed.

View the method explanation

Writing tips

Read each item and check those you have confirmed. Do not use the fictional example’s checkbox states as findings about your own program.

Read the full fictional example

Before writing, check when to use the worksheet and what completion involves

Synthetic control design worksheet is a worksheet for applying Synthetic Control Method in meetings or individual work. For the overall duration of the method, use “Illustrative planning durations, not common standards: collection 1–4 weeks, analysis/checks 2–4 weeks” as a reference, adjusting for scale and resources. Fill in what you know provisionally and leave unresolved matters as questions to check next.

When this worksheet is useful

Evaluate policies affecting one/few areas, countries or firms, such as city industrial policy or national regulation
Multiple similar untreated units and long pre-panels available
No single parallel-trend DiD comparator but combinations improve pre-fit

Participants and preparation

1–2 econometric/statistical specialists with skills such as R Synth

Bring the materials used for decisions, existing data, and differing stakeholder views to help complete the worksheet.

Suggested order

1. Basic informationEnter evaluation basics
2. Define treated units and donor poolEnter selection rationale
3. Variables for synthesisEnter weighting variables and rationale
4. Assess pre-fitEnter pre-fit results
5. Design checklist

Signs the worksheet is ready for review

Path plot: treated actual versus synthetic series
Donor weight list
Estimated post-gaps/effect magnitude

Cautions for review

Affected donors compromise counterfactuals. State selection grounds and exclude potentially exposed units.
Always optimize on pre-data only; using post-variables makes estimation circular.
Large gaps alone are not strong evidence. Consider placebo fit differences and donor counts; they do not remove confounding.

Read a completed fictional example (your entries will not change)

Fictional example; not actual results

A fictional calculation combining Cities B and C at 0.5 each. The short table illustrates weighting, not sufficient duration for valid research.

Basic information

Policy/intervention name: City A consultation scheme
Analyst: Analyst A (fictional)
Data sources: Consistently defined city-level use indicators (fictional)
Creation date: July 1, 2026 (fictional)

Reason for this entry: Compare definitions across cities.

Define treated units and donor pool

Scroll horizontally to view the table

Unit name	Treated / donor	Inclusion/exclusion reasons
City A	Intervention	Scheme introduction target
City B	Candidate donor	No scheme; spillovers to be checked
City C	Candidate donor	No scheme; spillovers to be checked

Reason for this entry: Candidates are not automatically eligible.

Variables for synthesis

Outcome name: Uses per 100 target people
Pre-outcome timepoints used: Illustration uses pre 1, pre 2 and pre 3
Additional predictive covariates: Consider population composition in actual analysis
Theoretical selection rationale: Align pre-factors affecting scheme use

Reason for this entry: Do not finalize variables from a short illustration alone.

Assess pre-fit

Pre-MSPE: mean squared prediction error: 1/3: fictional-table calculation
Visual fit: good / acceptable / insufficient: Undecided; 3 illustrative timepoints cannot establish sufficiency
Response to inadequate fit: Add periods and review donors/predictors

Reason for this entry: Small error alone does not establish validity.

Design checklist

☐ Not checked: Assessed construction/fit feasibility from pre-series length and variation
☐ Not checked: Excluded potentially exposed donors
☑ Checked: No post-data in weight optimization
☑ Checked: Checked pre-fit with MSPE and series plots
☐ Not checked: Conducted identical placebo analyses for all donors
☐ Not checked: Reported post/pre MSPE ratios
☑ Checked: Included donor weights in report

Reason for this entry: Weights 0.5/0.5 and pre-errors are illustrated; actual duration needs and placebos remain unconfirmed.

Common errors in judgment

Optimize weights using post-results

How to revise: Set weights from pre-data

Reason: Do not use the target post-gap to select comparators.

Call a large post-gap an effect despite poor pre-fit

How to revise: State poor fit and reconsider design

Reason: The counterfactual may already be poorly approximated before intervention.

Editorial and checking information

Content updated: 2026-09-13Source checked: 2026-09-13 (includes AI cross-checking)No documented human reviewIncludes fictional examplesEditorial policy and scope of checking Cite this page Report a correction

References and checked sections

Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program
Alberto Abadie, Alexis Diamond, and Jens Hainmueller · 2010
Section consulted
1. Introduction / Synthetic control methods
How it relates to the text
Uncertainty about whether combinations reproduce counterfactuals is not automatically resolved by aggregate data.
Source checked: 2026-09-13 (includes AI cross-checking)

How to cite this page

EvalCompass Editorial Team. “Synthetic control design worksheet”. EvalCompass, updated 2026-09-13. https://evalcompass.jun-nakatani.com/en/worksheets/synthetic-control/

Add the date you accessed the page. Text created by this site is available under CC BY 4.0. Quotations, external materials, and bundled fonts have their own terms of use. When reusing content, credit the source, link to the license, and indicate any changes.

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Change history

2026-09-13 — Checked sources for definitions and conditions and added fictional examples, judgment rationales and completed entries for every field. Revised unsupported historical or uptake claims. Reviewed procedures and cautions, adjusting fixed counts, thresholds and institutional generalizations. Preserved field positions, types and counts while revising fixed conditions and thresholds.