Conclusion
The Guess et al. (2023) study's null individual-level effects preclude meeting the framing's 10% population-level threshold regardless of aggregation assumptions.
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[DEFENSE-REBUT → 049yvk] The framing defines significant causal factor as one whose removal would reduce measured affective polarization growth by at least 10% of the observed change. Guess et al. (2023) found no detectable individual-level effect of algorithmic versus chronological feeds on affective polarization in a sample exceeding 23,000 participants. A null individual-level effect cannot aggregate to a non-null population-level effect; zero multiplied by any population size remains zero. Therefore, The Guess et al. (2023) study's null individual-level effects preclude meeting the framing's 10% population-level threshold regardless of aggregation assumptions. (Warrant: Population-level effects are aggregations of individual-level effects; if the individual-level effect is indistinguishable from zero, no aggregation procedure produces a population-level effect meeting any positive threshold.)
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- Could a different cause produce the same effect E in this case?Open
- Is there a plausible causal mechanism by which C could bring about E?Open
- How strong is the causal generalization linking C to E? Are there documented cases where C does not produce E?Open
- Are there intervening or confounding factors that could interfere with the causal chain from C to E?Open
- Is the apparent link between C and E merely a post hoc correlation rather than a causal relation?Open
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