CA-V2-18

Let me work through the causal structure carefully, suppressing linear one-way thinking. **The reverse-causal model states:** - Prolific posters produce more content → more total content attracts more total likes (aggregate) - But the *rate* (likes per post) is **lower** for prolific posters **Why?** Each additional post from a prolific poster dilutes quality — there's a finite amount of high-quality, engaging content any person can produce. As volume increases, average quality per post drops, and audience attention per post drops. More posts compete for the same follower base's limited attention. This is a negative feedback loop on per-unit quality: higher volume → lower per-unit engagement, which is precisely why the artificial-likes intervention failed — the real driver was content volume, and likes-per-post actually *decreases* with frequency. **The answer is (B) — Negative.** Each additional post dilutes quality and splits audience attention, so likes-per-post decreases as posting frequency increases. This is exactly what the data scientist's reverse-causal explanation predicts: prolific posters accumulate more *total* likes (because they have more posts), but each individual post receives *fewer* likes on average. The observational correlation between total likes and posting frequency was driven by volume, not by per-post engagement — which is why artificially boosting likes had almost no effect on posting behavior.

**The answer is (B) — Negative.** **Reasoning:** The reverse-causal model, as explicitly stated by the data scientist, works like this: ``` Prolific poster (intrinsic trait) → posts more content → more content accumulates more *total* likes → but each individual post competes for audience attention → likes-per-post is LOWER for high-frequency posters ``` The data scientist even states this directly: *"the likes-per-post rate is actually LOWER for prolific posters."* The reverse-causal model predicts posting frequency ↑ → likes/post ↓ — a **negative relationship**. Why the other options fail under this model: - **(A)** would require a skill/quality mechanism — but the model is about volume dilution, not skill accumulation - **(C)** would mean no structural relationship exists — contradicts the model's core mechanism - **(D)** requires a special non-monotonic structure with no basis in the stated model The intervention result (only 5% lift vs. 40% predicted) is the empirical fingerprint: artificially inflating likes barely moves posting because it's not likes that drive posting — it's the user's inherent tendency to post that drives both frequency *and* total likes. Each additional post from a prolific poster dilutes per-post engagement, making **(B)** the signature prediction of the reverse-causal model.