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Reposted by Richard McElreath 🐈‍⬛

Antonino Polizzi @polizzan.bsky.social · 1d

✍️ Just out: Annette Baudisch & I delve into the formal demography of fertility, birth, & reproduction timing. 👶 We use methods from mortality research to summarize when in the parental life course children are born. 📆 1/n
doi.org/10.1080/0032...
@sdu.dk @oxforddemsci.bsky.social @mpidr.bsky.social

Fertility, birth, reproduction: Connecting formal demographic frameworks

The conventional framework of fertility research conceptualizes childbirth from the mother’s perspective. From her perspective, birth is an uncertain and potentially recurring event. In contrast, t...

doi.org

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6h

I guess because software allows it, ppl keep trying to estimate diversification rates on phylogenies. This is not in principle possible, because infinite combinations of diversification and extinction rates can explain almost any tree. Short, clear recent-ish paper: www.nature.com/articles/s41...

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 1d

This is a nice paper. It faces down a common problem: in order to explain to why a common approach cannot work even in theory, we first need to teach a framework in which regression is not magic that tells us which variables on right cause the variable on left. It's exhausting.

Anyway great paper!

Peter Tennant @pwgtennant.bsky.social · 1d

academic.oup.com/ije/article/...

Analyses of ‘change scores’ do not estimate causal effects in observational data

AbstractBackground. In longitudinal data, it is common to create ‘change scores’ by subtracting measurements taken at baseline from those taken at follow-u

academic.oup.com

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Reposted by Richard McElreath 🐈‍⬛

Julia M. Rohrer @dingdingpeng.the100.ci · 2d

Happy to announce that I'll give a talk on how we can make rigorous causal inference more mainstream 📈

You can sign up for the Zoom link here: tinyurl.com/CIIG-JuliaRo...

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Reposted by Richard McElreath 🐈‍⬛

Aki Vehtari @avehtari.bsky.social · 2d

I'm looking for a doctoral student with Bayesian background to work on Bayesian workflow and cross-validation (see my publication list users.aalto.fi/~ave/publica... for my recent work) at Aalto University.

Apply through the ELLIS PhD program (dl October 31) ellis.eu/news/ellis-p...

ELLIS PhD Program: Call for Applications 2025

The ELLIS mission is to create a diverse European network that promotes research excellence and advances breakthroughs in AI, as well as a pan-European PhD program to educate the next generation of AI...

ellis.eu

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 2d

Someone sent this to me and I want to hug, um, share it with all of you (src @theunderfold.bsky.social )

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 5d

Thinking about and discussing this more with colleagues, I'd really like a continuous time solution, like the Gillespie algorithm but for perfect conterfactuals as defined in thread below. I can't find that this has been done, but somehow believe some rogue chemist has already worked it out

Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

Are we doing simulations wrong? This paper convinced me we are. doi.org/10.1098/rstb... Usually we run 2 sets of "worlds" w and w-out intervention. Gives large uncertainties that include negative (harm) effects of interventions that are actually always positive (beneficial)!

Figure 5. Time series showing cumulative number of cases averted at each time caused by the intervention calculated using our method (single-world) and a
standard method. Shaded regions denote 90% confidence intervals. Note that there is more variation in the middle of the epidemic, so it may seem as though the
number of cases averted is large during those times. (Online version in colour.)

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 5d

The most romantic sentence in the German language: Möchtest du mit mir eine Kartoffel essen gehen? (Today and tommorrow in Leipzig at Augustusplatz) kartoffelverband-sachsen.de/saechsisches...

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Reposted by Richard McElreath 🐈‍⬛

Julia M. Rohrer @dingdingpeng.the100.ci · 6d

Just finished reading this *excellent* article by Gabriel et al. which discusses which effects can be identified in randomized controlled trials. With DAGs!>

link.springer.com/article/10.1...

Elucidating some common biases in randomized controlled trials
using directed acyclic graphs

Although the ideal randomized clinical trial is the gold standard for causal inference, real randomized trials often suffer
from imperfections that may hamper causal effect estimation. Stating the estimand of interest can help reduce confusion
about what is being estimated, but it is often difficult to determine what is and is not identifiable given a trial’s specific
imperfections. We demonstrate how directed acyclic graphs can be used to elucidate the consequences of common imperfections,
such as noncompliance, unblinding, and drop-out, for the identification of the intention-to-treat effect, the total
treatment effect and the physiological treatment effect. We assert that the physiological treatment effect is not identifiable
outside a trial with perfect compliance and no dropout, where blinding is perfectly maintained

Table 1 showing the Identifiability of target estimands depending on whether there is blinding, full compliance, and no drop-out

An example DAG from the paper.
Fig. 4: A blinded trial with noncompliance.

U are unobserved confounders, Z is treatment assignment, C is compliance, X is the realized treatment, S is the subject's physical and mental health status, Xself and Xcln are the treatment that the participant and the clinician believed the participant received, Y is the outcome.

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

YES THE OLD MAGIC

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Reposted by Richard McElreath 🐈‍⬛

Richard Sever @richardsever.bsky.social · 6d

Another record month for bioRxiv - and further evidence the pandemic spike+dip was just that and growth continues. Thanks to all involved and that includes 🫵

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

The algorithm is more intensive than usual method. So some algorithm research is needed here. And getting ppl to do the right thing will need friendly tool support. The authors of paper provide their code, which is a great start to templating this to generic ABMs or ODEs. github.com/HopkinsIDD/c...

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

We want instead "perfect" counterfactuals that change only the intervention (and consequences of it) and compare across same worlds in all other events. This can be done by computing a potential event graph first, and then forking it and applying intervention logic.

Figure 3. ‘Single-world’ simulation process (continued). In order to measure the impact of the intervention on the epidemic, we use the PEG (figure 2) to construct two
epidemics: one uncontrolled (left), one with intervention (right). We start by setting the actual state (denoted by colouring the whole circle) of each individual at time t0
to their initial condition, and then changing their state according to the intervention, in this case setting p2 as vaccinated (dark green). We then prune events between t0
and t1: removing inconsistent events and allowing the intervention to stochastically remove events (b,c). We then use those events to determine the actual state of each
individual at t1, allow the intervention to alter that state, then prune inconsistent events again (d,e). We repeat the process for times t2 ( f,g), t3 (h,i) and so on. This final
graph can be used to extract our outcome of interest. Any graphs made from the same PEG represent the results of different interventions in a ‘single world’.

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

Are we doing simulations wrong? This paper convinced me we are. doi.org/10.1098/rstb... Usually we run 2 sets of "worlds" w and w-out intervention. Gives large uncertainties that include negative (harm) effects of interventions that are actually always positive (beneficial)!

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

So another example of this perhaps doi.org/10.1098/rsos...

The natural selection of bad science | Royal Society Open Science

Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just m...

doi.org

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 6d

I suspect researchers want to find p<0.05 and collinearity makes that harder, so they invented procedures for searching model space so they could get more p<0.05.

There is no statistical philosophy in which those procedures are good for prediction or inference. Good for publishing though

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 7d

In summary

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 7d

I am told that his Orthogonal series is his best work, so I might read those books next. But happy to receive opinions.

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 7d

I recently read Egan's Incandescence. This is a book that people hate or love. I liked it! It doesn't condescend to the reader, but expects you to pay attention and figure out the major plot links on your own. Also endless pages of aliens talking about geometry.

Not going to be a movie anytime soon

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 7d

My first attempt to dispel concern with this kind of naive correlation filtering: It can arise from different causes and vanish in a properly specified model. Example: Data generated by this DAG

X –> Z –> Y

can have highly correlated X and Z. But in model Y ~ X + Z it won't show any "collinearity"

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 7d

A serious failure case is that many biologists seem to have been taught that they should inspect bivariate correlations between predictors and not include pairs with high correlations. That is not even "collinearity" as statisticians define it (where is about joint info in additive model).

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 7d

Was asked about collinearity again, so here's Vahove's 2019 post on why it isn't a problem that needs a solution. Design the model(s) to answer a formal question and free your mind janhove.github.io/posts/2019-0...

tl;dr

Collinearity is a form of lack of information that is appropriately reflected in the output of your statistical model.
When collinearity is associated with interpretational difficulties, these difficulties aren’t caused by the collinearity itself. Rather, they reveal that the model was poorly specified (in that it answers a question different to the one of interest), that the analyst overly focuses on significance rather than estimates and the uncertainty about them or that the analyst took a mental shortcut in interpreting the model that could’ve also led them astray in the absence of collinearity.
If you do decide to “deal with” collinearity, make sure you can still answer the question of interest.

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Reposted by Richard McElreath 🐈‍⬛

Crystal Lewis @cghlewis.bsky.social · 8d

RDM Weekly Issue 15 is out! 📬

- Data Tracking in Neurodivergent Samples Guide from @jcbullen.bsky.social and colleagues
- Science as Amateur Software Development from @rmcelreath.bsky.social
- Project Management from @manybabies.org
and more!

rdmweekly.substack.com/p/rdm-weekly...

RDM Weekly - Issue 015

A weekly roundup of Research Data Management resources.

rdmweekly.substack.com

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 8d

powerful kiwi energy in this

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 8d

This covers some important sources of bias that arise from naive control strategies. Of value even if you don't care about the life-satisfaction ~ age association.

Tamkinat Rauf @tsrauf.bsky.social · 9d

Life satisfaction mostly declines with age. Previous findings (esp. the famous U-shaped age-SWB trajectory) were artifacts of misspecified models. doi.org/10.1093/esr/...

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