#causaldiagrams
Live, measured metrics for the hashtag #causaldiagrams from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #causaldiagrams
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 13:46 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-27 13:46 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 13:46 UTCEverything below is measured over the latest 3 public posts (spanning ~8960 hours).
Posting hours (UTC)
Languages: English (3)
Avg boosts / post: 11
Top of the latest posts
My #introduction: I repurpose observational #RealWorldData into scientific evidence for the prevention and treatment of human disease. At #CAUSALab, we often do so by explicitly emulating a #TargetTrial. Other times we analyze #RandomizedTr
"Draw your assumptions before your conclusions." 5 years ago we launched the first version of the #CausalDiagrams course via HarvardX and edX. This was the official trailer: https://www.youtube.com/watch?v=SB2FxG-SdEQ Since then, about 80,0
Remarks about #m-bias; bigger picture: why #longitudinal data are generally needed for #causalinference ( #causaldiagrams are not enough) https://go-bayes.github.io/b-causal/posts/m-bias/m-bias.html
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/causaldiagrams