Inspect the matched groups before accepting the campaign effect

Define the exposure and the population before matching

State the campaign exposure, outcome window and target population. Ask whether the effect concerns those actually exposed, everyone eligible or another clearly described group. A comparison of people who received a campaign is not automatically a claim about what would happen if every customer received it. Keep the exposure definition and eligibility date fixed in the review record.

MatchIt’s planning guidance distinguishes target effects and emphasizes measured pre-treatment covariates. Its introductory documentation defines a propensity score as the predicted probability of treatment given included covariates. The score is therefore not a general customer similarity certificate or a prediction of campaign success. Ask why the selected variables are appropriate to the causal question and whether their timing precedes exposure.

Inspect who can actually be compared

Request a flow from eligible records to included records, with the reasons observations were excluded or received different influence. Inspect where exposed customers have plausible unexposed counterparts and where they do not. A specialist should explain the overlap problem in audience terms, such as a campaign sent almost exclusively to very recent buyers, rather than hiding it behind a single matching score.

MatchIt’s method guidance explains that pruning or weighting can change the sample and, for some methods, the population represented by the effect. Retain that consequence in the reporting brief. Do not describe a result for a narrow retained subset as the effect on all customers merely because the original dataset was large. If the target population cannot be supported, narrow the claim or seek a different design.

Review the covariates themselves, not just the score

MatchIt’s balance guidance recommends examining covariate distributions and warns that balanced propensity scores need not imply balanced covariates. It favors descriptive balance evidence over hypothesis-test p-values alone. Use that guidance to request a readable before-and-after view of the characteristics that matter for this campaign comparison.

Ask the analyst to identify material residual differences, not only an average improvement across many fields. A similar mean purchase history can conceal different tails or customer mixes. Agree which diagnostics and interactions are relevant to the design without imposing a universal cutoff from a checklist. Review the matching design before inspecting a preferred effect estimate where feasible, so the team does not select whichever version produces the most flattering campaign result.

Keep unresolved confounding and uncertainty in the decision

Balanced recorded variables cannot prove that unrecorded motivation, access or purchase intention was comparable. Ask which plausible common causes of exposure and outcome remain unmeasured and how those concerns affect interpretation. Separate a useful adjusted association from a causal claim whose assumptions need stronger support. Matching also does not repair incorrect exposure logs or an outcome window that differs between groups.

Require effect estimation and uncertainty appropriate to the actual matching design, including any reuse, grouping or weighting the analyst chose. Preserve the matching specification, retained counts, diagnostics and sensitivity decisions with the result. If the evidence remains weak, say which additional design or measurement would address the practical question. A qualified limitation is more useful than renaming an observational study an experiment after the matching step.

Make the reasoning concrete.

A fictional retailer sends a catalog mainly to recent repeat buyers. A supplier matches recipients and nonrecipients on a predicted exposure score, but the retained groups still differ in recent purchase frequency and many first-time buyers disappear from the comparison.

The retailer requests a revised balance and population report and does not announce a population-wide incremental effect from the matching label alone.

Take the questions into your next brief.

Use these prompts in your own notes. Nothing is submitted from this page.

1. Target

   Whose effect and which exposure/outcome window are intended?

2. Timing

   Are the adjustment variables relevant and pre-exposure?

3. Overlap

   Who is excluded or weakly represented after matching?

4. Balance

   Which important covariate differences remain?

5. Limit

   What unmeasured confounding and design-aware uncertainty constrain the claim?

Watch for the wrong conclusion.

• Calling observational matching randomization.
• Reviewing only the propensity-score distribution.
• Reporting a retained-subset effect as a result for the original whole population.

Sources & scope

Review workflows, worksheets and fictional marketing examples are original editorial analysis. Named documentation supports only the attributed statistical concepts. Qualified analysts must assess assumptions and implementation; no actual data analysis, causal outcome or human reviewer is claimed.

• MatchIt: Getting Started →Primary statistical software and methodological documentation · Supports: Supports pre-treatment covariate planning, target effects and the definition of the propensity score. The fictional catalog example and review artifact are original.
• MatchIt: Matching Methods →Primary statistical software and methodological documentation · Supports: Supports preprocessing, pruning/weighting and resulting estimand consequences, plus design evaluation without selecting on outcomes. No method code or comparative performance claim reproduced.
• MatchIt: Assessing Balance →Primary statistical software and methodological documentation · Supports: Supports distributional balance review, the insufficiency of propensity-score balance and limitations of balance hypothesis tests. No numerical threshold or source dataset result reproduced.