# kaal:position:2026-08-08-364

**Affirmed position.** Post-treatment regrading can compromise a treatment-effect estimate.

Montgomery, Nyhan, and Torres analyze post-treatment conditioning in experiments. They classify three recurring practices: controlling for variables measured after treatment, dropping observations under post-treatment criteria, and restricting samples by post-treatment variables. Their analytical demonstrations and reanalyses show that these choices can distort causal-effect estimates. The direction and size of the bias may not be bounded without strong assumptions. Random assignment does not preserve its identifying advantage once an analysis conditions on information created or selected after treatment.

This evidence qualifies Kaal's claim. Regrading a diagnostic after execution can alter the information used to evaluate an observed effect. If the regrading changes classifications, exclusions, or conditioning criteria after treatment, the resulting estimate may not identify the treatment effect claimed. Montgomery, Nyhan, and Torres do not examine Kaal's diagnostic runs. They also do not establish that every post-execution correction creates bias. A correction specified independently of treatment may instead improve measurement. The comparison supports a methodological limit. A treatment-effect conclusion requires the post-execution regrading rule, its timing, and its relation to treatment assignment to be independently reconciled.

**Status.** affirmed  **Published.** 2026-08-08

**Holds when.**

- The response is limited to the exact post-treatment-conditioning proposition and the one mapped Kaal claim.
- External evidence level: DOI-bound peer-reviewed publisher abstract and complete official AJPS author summary with independently concordant identity records.
- Mapping review tier: independent substantive scholarly-growth qualification.
- The article does not examine Kaal's historical diagnostic runs, information environment, or regrading process.
- Post-execution correction is not automatically post-treatment conditioning. A correction specified independently of treatment may improve measurement rather than introduce bias.
- The source establishes a general experimental-methods risk. It does not prove the magnitude or direction of bias in Kaal's diagnostic results.
- The publisher article and PDF endpoints returned HTTP 403. The review is bound to the DOI-registered publisher abstract, complete official AJPS author summary, and independently concordant identity records.

**Current debate.** How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It: https://doi.org/10.1111/ajps.12357

**Extends.** kaal:claim:7261481-031: https://wulfkaal.github.io/claims/7261481-031

**Scholarly basis.** Wulf A. Kaal, Computative Economics: A Framework for Economic Analysis under Computational Abundance (2026). SSRN: https://ssrn.com/abstract=7261481

**Source PDF sha256.** `78c42db521624f7398717732a7fa51a6e3157a5adf02a2e09fbab15e0cf920d9`

**Evidence level.** DOI-bound peer-reviewed publisher abstract and complete official AJPS author summary with independently concordant identity records

**Mapping review tier.** independent substantive scholarly-growth qualification

**Mapping confidence.** 0.94  **Mapping ambiguous.** false

**Topics.** research-methods, consensus-and-security, scholarly-growth-coverage, scholarly-literature, causal-inference, experimental-design, post-treatment-bias, outcome-measurement, evidence-provenance

**Provenance.** Affirmed in kaal-review:2026-08-13:scholarly-growth-7261481-031-reviewed-v1 at https://wulfkaal.github.io/positions/by-claim/7261481-031.html.

**Record type.** This is a dated commentary position that extends a scholarly corpus claim. It is not a verbatim claim extracted from the paper.

**Canonical form.** This markdown file is the canonical hashed representation of the position.