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Followers
196
Account age
3 yrs
💰 Suggested sponsorship rate
$40 – $65 / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
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Known for
📊 Post engagement
2
Avg engagement / post
1%
Engagement vs followers
Jul 2023
On Mastodon since
🔥 Top post: v2.1 of `delicatessen` out now :python: 🥳 Some highlights: - Ad · 5 likes + reposts
📊 Activity & format
Posting cadence
0.95 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 10 text · 2 image · 0 video.
Follower / following
1.2×
Follows 169 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
I can't go anywhere without my emotional support vector machine
f you're curious about how to apply M-estimation, we have a tutorial paper in IJE that goes through the basics and provides SAS, R, Python code/example
https://academic.oup.com/ije/article/53/2/dyae030/7616672
if you're curious about how to approach causal effect estimation from an estimating equation approach, I've replicated chapters 12-14 of the Hernan & Robins book
https://deli.readthedocs.io/en/latest/Examples/Hernan-Robins-2023.html#
Photo post
The paper provides an in-depth example, a simulation study, and additional discussion of how the synthesis approach relates to other methods (like bounds). I also have a pre-print that is a follow-up on this work. The pre-print considers a…
Now we get to the main contribution. As an alternative, I propose a synthesis of statistical and mathematical models. This approach is a generalization of the previous two. Importantly, it allows one to integrate external information via t…
Standard solutions to positivity are to restrict the target population or restrict the covariate set. In the example, restricting the target population is methodologically defensible but it is limited in the guidance it provides for the cl…
This exchangeability assumption comes paired with a positivity assumption. The positivity assumption requires that all relevant covariate patterns in the target population also occur in the study population. The paper revolves around what …
Transportability methods generally proceed under an exchangeability (or ignorability assumption). We might say that given the covariate X, the study population and target population are interchangeable. To quote from another of my papers, …
Transportability methods apply to the scenario where the study population doesn’t overlap in time or space with our target population (the population we are interested in drawing inference for). A simple example of this is a randomized tri…
The Jan issue of Epidemiology features my paper on addressing positivity violations for transportability problems. The main contribution there is the proposed synthesis method. Since I really like that paper, I am going to provide an overv…
🐘 Community & instance
Home server
fediscience.org
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
✅ Link-verified
Verified link
Proved ownership of a website linked on their profile — Mastodon's green-check verification, a real identity signal rather than a paid badge.
On Mastodon since
Jul 2023
An established account with real history on the platform.
💡 Facts
🗓️Joined Mastodon in 2023 — 3 years ago.
👁️Averages 2 views per post.
📤Posts about 1× per week.
🕵️ Fake follower check
Estimated75/100
Good
Credibility score
92%
Real
Real audience
Low
Fake-follower risk
High
Data confidence
- Est. 92% real, active audience · Low fake-follower risk.
- Engagement (~1.0% of followers engage each post) is around typical for Mastodon.
- Verified account.
- Established account (3+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Paul Zivich, assistant professor
Computational epidemiologist, causal inference researcher, amateur mycologist, and open-source enthusiast.
#epidemiology #statistics #python
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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