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Research & Evidence

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40 results
  1. MSC-P-001How do you turn a marketing claim into a testable question?Decision Science↗
  2. MSC-P-002Correlation or causality: what can an analysis actually support?Marketing Measurement↗
  3. MSC-P-003How should uncertainty in a marketing result be expressed?Decision Science↗
  4. MSC-P-004Statistical significance or effect size: which result should be interpreted?Decision Science↗
  5. MSC-P-005How do you measure a marketing construct that is not directly observable?Market Research↗
  6. MSC-P-006How do you design and validate a measurement scale?Market Research↗
  7. MSC-P-007Alpha or omega: how should scale reliability be assessed?Market Research↗
  8. MSC-P-009PCA, EFA or CFA: which method should you choose?Market Research↗
  9. MSC-P-010When should you run a marketing experiment?Marketing Measurement↗
  10. MSC-P-011How do you design an A/B test that actually estimates an effect?Marketing Measurement↗
  11. MSC-P-012How many observations does an experiment need?Decision Science↗
  12. MSC-P-013How do you measure campaign incrementality with a control group?Marketing Measurement↗
  13. MSC-P-017How do you detect selection, contamination and attrition in an experiment?Marketing Measurement↗
  14. MSC-P-018Predictive or causal regression: what are you trying to estimate?Marketing Models↗
  15. MSC-P-019How do you diagnose a marketing regression before interpreting it?Marketing Models↗
  16. MSC-P-022How do you estimate price elasticity and its uncertainty?Pricing Science↗
  17. MSC-P-026Logit vs Probit: how do you choose for purchase probability?Customer Science↗
  18. MSC-P-029Which customers have the highest probability of churn?Customer Science↗
  19. MSC-P-027TAM, UTAUT or UTAUT2: which framework should be used to study technology acceptance?Market Research↗
  20. MSC-H-001Measurement and causality: how can a marketing effect be established?Marketing Measurement↗
  21. MSC-H-002Marketing response models: shape, delay and saturationMarketing Models↗
  22. MSC-H-003Pricing science: connecting price, demand and contributionPricing Science↗
  23. MSC-H-004Customer and choice science: behavior, value and heterogeneityCustomer Science↗
  24. MSC-H-005Measurement science: building valid indicatorsMarket Research↗
  25. MSC-H-006Statistical decision methods: choose, quantify, validateDecision Science↗
  26. MSC-P-008How do you validate a marketing measurement scale?Market Research↗
  27. MSC-P-014How do you design a marketing geo experiment?Marketing Measurement↗
  28. MSC-P-015How do you estimate an effect with difference-in-differences?Marketing Measurement↗
  29. MSC-P-020How do you address price endogeneity?Pricing Science↗
  30. MSC-P-021Fixed or random effects: which panel model should you choose?Marketing Models↗
  31. MSC-P-023How do you estimate a demand function?Pricing Science↗
  32. MSC-P-024How do you simulate a price-volume-margin scenario?Pricing Science↗
  33. MSC-P-028How do you estimate CLV with BG/NBD and Gamma-Gamma?Customer Science↗
  34. MSC-P-030How do you analyze retention with a survival model?Customer Science↗
  35. MSC-P-031How do you build a useful customer segmentation?Customer Science↗
  36. MSC-P-032How do you test segmentation stability?Customer Science↗
  37. MSC-P-033How do you validate a marketing forecast?Decision Science↗
  38. MSC-P-034How do you build a Monte Carlo simulation for a marketing decision?Decision Science↗
  39. MSC-P-035How do you model saturation and adstock?Marketing Models↗
  40. MSC-P-039Which statistical test should you choose?Decision Science↗
← All methods
METHOD DOSSIERMSC-P-017Experimentation and causalityVerified scientific dossier

How do you detect selection, contamination and attrition in an experiment?

The diagnostic follows units from eligibility to outcome: assignment, exposure, contamination, missingness and exclusions. ITT remains based on assignment.

Scientific editorial team : Marketing Science Center

Direct answer

Characterize threats and run sensitivity analyses.

The diagnostic follows units from eligibility to outcome: assignment, exposure, contamination, missingness and exclusions. ITT remains based on assignment.

CONSORT 2010 Explanation and ElaborationHernán & Robins, Causal Inference: What If

01

Operational summary

Scientific question and scope

How do you detect selection, contamination and attrition in an experiment?

Supported

Characterize threats and run sensitivity analyses.

Forbidden

Automatically repair severe differential attrition.

02

Three reading levels

  1. 01

    Decision-maker — Connect the result to a declared decision, useful threshold and error cost.

  2. 02

    Practitioner — Fix population, unit, horizon, available variables and analysis rule before calculation.

  3. 03

    Analyst — Reproduce the calculation, quantify uncertainty and document diagnostics, failures and sensitivities.

03

Concrete marketing situation

The diagnostic follows units from eligibility to outcome: assignment, exposure, contamination, missingness and exclusions. ITT remains based on assignment.

04

Scientific question and scope

How do you detect selection, contamination and attrition in an experiment?

Diagnostic evidence, not a new causal effect

Required data

eligible unit through analysis flow

Population, unit of analysis, origin date and horizon must be declared in the deliverable. Without them, the estimand silently changes.

05

Why a simple analysis can fail

  • Automatically repair severe differential attrition.
  • Prespecified allocation and sample-ratio-mismatch test when implemented
  • Differential attrition
  • Spillover map

06

Method intuition

The method does not automatically turn an association into evidence. It links a declared question to an estimand, specification, compatible data and a bounded interpretation rule.

Diagnostic evidence, not a new causal effect

07

Required data

Scientific symbol dictionary

assigned_arm
Number of eligible units randomized to the declared arm. Unit: unit · Type: nonnegative integer · Role: input
observed_arm
Number of assigned units with the primary outcome observed under the prespecified missing-outcome rule. Unit: unit · Type: nonnegative integer · Role: input
exposed_arm
Number of assigned units that received the measured exposure under the prespecified exposure definition. Unit: unit · Type: nonnegative integer · Role: input
attrition_arm
Share of assigned units without an observed primary outcome in that arm. Unit: proportion · Type: number in [0, 1] · Role: output
exposure_T
Exposure share among treatment-assigned units. Unit: proportion · Type: number in [0, 1] · Role: diagnostic
contamination_C
Exposure share among control-assigned units. Unit: proportion · Type: number in [0, 1] · Role: diagnostic

Exact sealed engine inputs

assigned_treatment
Value: 5000 · Unit: unit · Type: integer · Data status: synthetic
assigned_control
Value: 5000 · Unit: unit · Type: integer · Data status: synthetic
observed_treatment
Value: 4800 · Unit: unit · Type: integer · Data status: synthetic
observed_control
Value: 4900 · Unit: unit · Type: integer · Data status: synthetic
exposed_treatment
Value: 4500 · Unit: unit · Type: integer · Data status: synthetic
exposed_control
Value: 200 · Unit: unit · Type: integer · Data status: synthetic

08

Formal model

Formal model

attrition_treatment = 1 − observed_treatment/assigned_treatment; attrition_control = 1 − observed_control/assigned_control; differential_attrition = attrition_treatment − attrition_control; treatment_exposure = exposed_treatment/assigned_treatment; control_contamination = exposed_control/assigned_control

Evidence claims: MSC-P017-C01 · MSC-P017-C02 · MSC-P017-C03 · MSC-P017-C04 · MSC-P017-C05

Scientific question and scope

Diagnostic evidence, not a new causal effect

09

Declared calculation

  1. 01

    Reconcile eligible, assigned, observed and exposed counts by randomized arm and reject impossible count relationships.

  2. 02

    Compute arm-specific attrition, differential attrition, treatment exposure and control contamination rates.

  3. 03

    Report implementation diagnostics only; do not claim that these rates repair bias or constitute an SRM test.

10

Numerical example or application case

Application example — synthetic data or declared parameters; no real observations

Sealed inputs

  • assigned_treatment=5000 [unit]
  • assigned_control=5000 [unit]
  • observed_treatment=4800 [unit]
  • observed_control=4900 [unit]
  • exposed_treatment=4500 [unit]
  • exposed_control=200 [unit]

Reproducible results

  • attrition_treatment/control=0.04/0.02
  • differential_attrition=0.02
  • exposure/contamination=0.90/0.04

Verified dossier: claims are linked to passage-level sources, the Python/R calculation is reproduced, limits are explicit, and independent scientific review is complete.

11

Validity assumptions

  • Do assigned counts reconcile with the randomization log and prespecified allocation?
  • Is outcome missingness compared by assignment arm under a declared rule?
  • Are exposure and contamination defined without conditioning the primary ITT analysis on post-assignment behavior?

12

Diagnostics and uncertainty

Diagnostics and uncertainty

Prespecified allocation and sample-ratio-mismatch test when implemented · Differential attrition · Spillover map · The sealed illustration reports counts and rates but does not compute an SRM p-value

Bounded diagnostic

These diagnostics reveal implementation risks; they do not repair bias.

Explicit uncertainty contract · not_estimable_from_sealed_inputs

Method : Explicit non-estimability assessment against the sealed input schema.

Target : Sampling uncertainty of attrition, exposure and contamination rates.

Engine evidence : diagnostic=diagnostics_do_not_repair_bias

Interpretation : Aggregate flow counts alone do not identify the dependence or missingness process needed for valid uncertainty intervals.

Stop when

Automatically repair severe differential attrition.

13

Result interpretation

  • Characterize threats and run sensitivity analyses.
  • The result is conditional on the declared population, horizon, specification and diagnostics. It must not be extended to another decision without new justification.

14

Supported and forbidden conclusions

Supported

Characterize threats and run sensitivity analyses.

Forbidden

Automatically repair severe differential attrition.

15

Possible marketing decision

  1. 01

    Characterize threats and run sensitivity analyses.

  2. 02

    The result is conditional on the declared population, horizon, specification and diagnostics. It must not be extended to another decision without new justification.

16

When to use — when to stop

Use when

Characterize threats and run sensitivity analyses.

eligible unit through analysis flow

Stop when

Automatically repair severe differential attrition.

Methodological alternatives

  • Run an exact sample-ratio-mismatch test when allocation probabilities and assignment logs are available.
  • Use sensitivity bounds for missing outcomes or noncompliance.
  • Report both ITT and prespecified treatment-received analyses without replacing the ITT estimand.

17

Reproducibility contract

Python and R are the executable references. SPSS and SAS remain secondary syntaxes until checked on the same data, specification and diagnostics.

The engine selects only this page’s explicit slice and branch. Its hash, outputs, and diagnostics remain sealed in the atomic dossier; no generic fallback branch is allowed.

Slice fingerprint: 86981a5527088e6e95da14a6c0091530f1f492c73e0b0dec47bdbd5cbda693e0

Formal model

attrition_treatment = 1 − observed_treatment/assigned_treatment; attrition_control = 1 − observed_control/assigned_control; differential_attrition = attrition_treatment − attrition_control; treatment_exposure = exposed_treatment/assigned_treatment; control_contamination = exposed_control/assigned_control

Verified dossier: claims are linked to passage-level sources, the Python/R calculation is reproduced, limits are explicit, and independent scientific review is complete.

18

Expected final deliverable

  1. 01

    How do you detect selection, contamination and attrition in an experiment? — eligible unit through analysis flow

  2. 02

    assigned_arm · observed_arm · exposed_arm · attrition_arm · exposure_T · contamination_C

  3. 03

    attrition_treatment = 1 − observed_treatment/assigned_treatment; attrition_control = 1 − observed_control/assigned_control; differential_attrition = attrition_treatment − attrition_control; treatment_exposure = exposed_treatment/assigned_treatment; control_contamination = exposed_control/assigned_control

  4. 04

    Prespecified allocation and sample-ratio-mismatch test when implemented · Differential attrition · Spillover map · The sealed illustration reports counts and rates but does not compute an SRM p-value

  5. 05

    Characterize threats and run sensitivity analyses. / Automatically repair severe differential attrition.

19

Scientific sources and evidence status

Verified dossier: claims are linked to passage-level sources, the Python/R calculation is reproduced, limits are explicit, and independent scientific review is complete.

  1. MSC-P017-C01 — Sample-size determination must name the primary outcome and all quantities used.
  2. MSC-P017-C02 — Attrition and non-compliance adjustments must be reported.
  3. MSC-P017-C03 — Analysis by randomized assignment preserves the intended comparison.
  4. MSC-P017-C04 — Consistency links the observed outcome to the potential outcome under the assigned policy.
  5. MSC-P017-C05 — The causal estimand assumes one customer's assignment does not alter another customer's outcome.
  1. CONSORT 2010 Explanation and Elaboration
  2. Hernán & Robins, Causal Inference: What If

Method connections

Parent territoryMeasurement and causality: how can a marketing effect be established?

Read next

MSC-P-010When should you run a marketing experiment?→MSC-P-011How do you design an A/B test that actually estimates an effect?→MSC-P-013How do you measure campaign incrementality with a control group?→