Evidence · Advanced · About 6 min
Test causal claims
Compare explanations, not just the order of events
Both sides · Before the roundRead the fictional case: The four-day workweek
Resolution A four-day, 32-hour standard work week should replace the current five-day, 40-hour model
Fictional round packet: 24 volunteer workplaces, covering offices, retail and distribution, joined a six-month comparison. Half were assigned by lottery to 32 hours at unchanged weekly pay; half kept 40 hours. Both groups maintained an output index of 100. Absence averaged three days per worker in the shorter-week group and five in the comparison group; these counts were not adjusted for scheduled days. Customer coverage was maintained, but payroll in the shorter-week retail and distribution sites was 8% higher; office payroll was unchanged. Healthcare and other continuous-service industries were not included. For this practice round, the affirmative defends replacing the normal 40-hour model with a 32-hour standard at unchanged weekly pay; it does not claim every shift must be identical. The negative challenges whether the benefits justify the costs of that replacement.
A result occurring after a change does not by itself establish what caused it. Examine the proposed mechanism, the comparison and plausible alternative explanations.
A stronger design can make one explanation more credible. Distinguish allocating participants to groups from selecting who enters the study: those choices affect different inferences. Causal reasoning can strengthen a case as well as challenge one.
Explore an example before trying
Inspect the fictional source
Random allocation makes some starting differences less likely to explain the group difference, though a small trial still has limits.
Use the group comparison as support. Keep clear which scheduling package was tested. These are days per worker, not a rate adjusted for scheduled days or a direct health measure.
Random allocation within a selected trial does not make the workplaces representative of all industries.
Name the mechanism
Explain how the proposed cause could produce the outcome.
Inspect the comparison
Ask what would have happened without the change.
Test the alternative
Identify another explanation and which evidence distinguishes it.
Your response
Negative · RebuttalChallenge the causal forecast from the employer review without denying the recorded gains. Explain how the challenge affects your case.
Read the fictional case: AI and the future of work
Resolution Advanced AI will on net improve outcomes for workers
Fictional round packet: a two-year review of 30 employers records 300 retained workers receiving 4% higher inflation-adjusted pay and working 37 rather than 40 hours per week after AI adoption. Sixty entry-level roles were removed and 40 technical roles added. Of the 60 displaced workers, 18 entered the new roles, 24 found work elsewhere at 8% lower pay, and 18 were still unemployed six months later. The other 22 new roles went to different applicants. The review does not isolate AI from every other change or forecast the whole economy. The affirmative argues that productive tools can improve pay and time for many workers. The negative argues that concentrated losses and barriers to new work can outweigh those gains.
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The review records… It does not separate… That changes… My remaining argument is…
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Allocation and generalization do different work
Resolution A four-day, 32-hour standard work week should replace the current five-day, 40-hour model
Fictional round packet: 24 volunteer workplaces, covering offices, retail and distribution, joined a six-month comparison. Half were assigned by lottery to 32 hours at unchanged weekly pay; half kept 40 hours. Both groups maintained an output index of 100. Absence averaged three days per worker in the shorter-week group and five in the comparison group; these counts were not adjusted for scheduled days. Customer coverage was maintained, but payroll in the shorter-week retail and distribution sites was 8% higher; office payroll was unchanged. Healthcare and other continuous-service industries were not included. For this practice round, the affirmative defends replacing the normal 40-hour model with a 32-hour standard at unchanged weekly pay; it does not claim every shift must be identical. The negative challenges whether the benefits justify the costs of that replacement.
- Comparison
Volunteer workplaces were assigned by lottery to the shorter-week package or the 40-hour model.
The allocation helps compare the groups within the trial.
- Affirmative · Supported inference
Lower absence in the shorter-week group gives more support to the scheduling package than a before-and-after increase alone would.
The comparison makes some alternative explanations less persuasive.
- Negative · Remaining limit
Volunteer selection and the missing healthcare sites still matter when extending the result to a general standard.
Random allocation does not settle which workplaces the finding represents.
Use a design’s strength for the inference it actually improves.
Test the reasoning
Read the fictional practice case
Resolution A four-day, 32-hour standard work week should replace the current five-day, 40-hour model
Fictional round packet: 24 volunteer workplaces, covering offices, retail and distribution, joined a six-month comparison. Half were assigned by lottery to 32 hours at unchanged weekly pay; half kept 40 hours. Both groups maintained an output index of 100. Absence averaged three days per worker in the shorter-week group and five in the comparison group; these counts were not adjusted for scheduled days. Customer coverage was maintained, but payroll in the shorter-week retail and distribution sites was 8% higher; office payroll was unchanged. Healthcare and other continuous-service industries were not included. For this practice round, the affirmative defends replacing the normal 40-hour model with a 32-hour standard at unchanged weekly pay; it does not claim every shift must be identical. The negative challenges whether the benefits justify the costs of that replacement.
Review and continue
Resolution Advanced AI will on net improve outcomes for workers
Read the practice task and fictional case
Challenge the causal forecast from the employer review without denying the recorded gains. Explain how the challenge affects your case.
Fictional round packet: a two-year review of 30 employers records 300 retained workers receiving 4% higher inflation-adjusted pay and working 37 rather than 40 hours per week after AI adoption. Sixty entry-level roles were removed and 40 technical roles added. Of the 60 displaced workers, 18 entered the new roles, 24 found work elsewhere at 8% lower pay, and 18 were still unemployed six months later. The other 22 new roles went to different applicants. The review does not isolate AI from every other change or forecast the whole economy. The affirmative argues that productive tools can improve pay and time for many workers. The negative argues that concentrated losses and barriers to new work can outweigh those gains.
Your response
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One model response
The review records higher real pay and shorter hours for retained workers after AI adoption. It does not separate every other change at those employers, so the affirmative should defend the mechanism and reach of its forecast rather than call AI’s economy-wide benefit established. This lowers confidence in the broader prediction; it does not erase the observed gains. Our displacement comparison remains an independent reason to challenge the overall balance.
The observational review does not isolate the tool’s contribution.
The response identifies what the limitation changes.
The negative can also develop a comparison of outcomes.
The causal challenge has a defined effect on confidence. It is combined with a substantive negative reason rather than treated as proof that AI caused no gain.
Review the performance
- EvidenceI distinguish the observation from its cause.
- ArgumentsI identify what the comparison does not isolate.
- RefutationI explain the challenge’s significance without exaggerating it.
Choose one move to improve, revise your response, then try it aloud.
Common mistakes to avoid
- Treating a possible alternative explanation as proven.
- Confusing random allocation with a representative sample.
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