Carlos Poses
@carlosgposes.bsky.social
55 followers 120 following 13 posts
PhD candidate at UMC Utrecht. Causal inference, real world evidence
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carlosgposes.bsky.social
Looking forward to the development of fourthly robust, fifthly robust and why not twenty-seventhly robust estimators to keep up with
Reposted by Carlos Poses
thomvolker.bsky.social
Cool stuff!

Florian van Leeuwen and I implemented a prediction function in the #mice package that allows the incorporation of missing data uncertainty in a prediction interval.

The `predict_mi()` function is available in the current development version: github.com/amices/mice

#Rstats #statsky
Image of R code. To reproduce:

library(ggplot2)
library(dplyr)

library(mice, warn.conflicts = FALSE)
 
imp <- mice(nhanes, m = 5, maxit = 5, seed = 1, 
            ignore = rep(c(FALSE, TRUE), c(20, 5)), 
            print = FALSE)
 
impdats <- complete(imp, "all")
 
train <- lapply(impdats, function(dat) subset(dat, !imp$ignore))
test <- lapply(impdats, function(dat) subset(dat, imp$ignore))
 
fits <- lapply(train, function(dat) lm(age ~ bmi + hyp + chl, data = dat))
preds <- predict_mi(object = fits, newdata = test, pool = TRUE, interval = "prediction")
 
preds
 
preds %>% 
  as.data.frame() %>% 
  mutate(case = 1:nrow(preds),
         y = test[[1]]$age) %>% 
  ggplot(aes(x = fit, y = case, col = rowSums(is.na(nhanes[imp$ignore,]))>0)) +
  geom_point() +
  geom_errorbar(aes(xmin = lwr, xmax = upr)) +
  theme_minimal() +
  scale_color_manual(values = mice::mdc(1:2), labels = c("observed", "missing")) +
  theme(legend.title = element_blank(),
        legend.position = "bottom") +
  labs(x = "prediction",
       title = "Pooled prediction intervals")
Reposted by Carlos Poses
carlosgposes.bsky.social
Haha. Glad we are on the same wavelength.
carlosgposes.bsky.social
Hope it’s not too late to recommend Infinite Powers, by @stevenstrogatz.com . It’s fun and very well written!
carlosgposes.bsky.social
A lot of the Target Trial Emulation framework is 'just' translating ideas from causal inference with observational data into ideas that are easier to digest and think through, even if you already know the hard stuff.

Now it's time to do some studying and apply these ideas to my own research!
carlosgposes.bsky.social
2. Communication is key in science. Just Robins 'g-formula' would conceptually cover a lot of what we did. But I doubt anybody who is presented with just the g-formula would be able to think clearly about their causal question in terms of treatment strategies, postrandomization events, and so forth.
carlosgposes.bsky.social
Some thoughts after the course:
1. It is very important to think about treatment strategies and how they develop over time. Not commonly done, but often the inference of most interest in trials is about time-varying treatment strategies, not interventions at one time-point.
carlosgposes.bsky.social
Last week I was lucky enough to participate in the Target Trial Emulation course at @causalab.bsky.social. It was a really nice experience! The instructors were excellent, as were the teaching materials, and they covered a great deal of content. I would really recommend it.
carlosgposes.bsky.social
Looks good. Thanks!
carlosgposes.bsky.social
Can I ask, what would a good 'second' book be in your opinion?
Reposted by Carlos Poses
bengoldacre.bsky.social
"Have you thought about applying the @opensafely model, for data privacy and efficiency, to non-health data?"

YES WE HAVE

Behold the new... OpenSAFELY-Schools!!

schools.opensafely.org
OpenSAFELY Schools
A collaboration between the National Institute of Teaching and the Bennett Institute for Applied Data Science
schools.opensafely.org
Reposted by Carlos Poses
smaglia.bsky.social
Are you interested in improving the #interpretability, #robustness and #safety of current AI systems with #causality and #RL?

Apply to our PhD position in Amsterdam 🚲🌷🇳🇱

Deadline: June 15
Reposted by Carlos Poses
thomvolker.bsky.social
Very happy to announce that the R-package `densityratio` is on CRAN! It implements non-parametric distribution comparison through density ratio estimation, which is useful for sample selection bias adjustment, two-sample testing and more!
See thomvolker.github.io/densityratio for vignettes and info!
Distribution Comparison Through Density Ratio Estimation
Fast, flexible and user-friendly tools for distribution comparison through direct density ratio estimation. The estimated density ratio can be used for covariate shift adjustment, outlier-detection, c...
thomvolker.github.io
Reposted by Carlos Poses
oisinryan.bsky.social
Still some spots available in our summer school on all things causal inference, 7-11 July in Utrecht! Discounts for those working in universities and non-profits, and affordable accommodation offered by @utrechtuniversity.bsky.social summer school!
Reposted by Carlos Poses
revenergetica.bsky.social
¡Pues ya estamos! ¡¡¡Primera vez en la historia!!! La energía eólica y la solar cubren más del 100% de la demanda en España peninsular. A las 11h05 el 100,09%. Todas las renovables, tambien récord, 115,14%

El sobrante se exporta y almacena.
carlosgposes.bsky.social
Regresión a la media :)
Reposted by Carlos Poses
maartenvsmeden.bsky.social
NEW PAPER

Really glad to see this one in print: the harm due to class imbalance corrections in prediction models developed using ML/AI

Excellently led by @alcarriero.bsky.social

onlinelibrary.wiley.com/doi/epdf/10....
Reposted by Carlos Poses
eurocim.bsky.social
🎉 Happy New Year! 🎉

Kickstart 2025 with exciting news! 🌟
Registrations for EuroCIM 2025 are now OPEN! Secure your spot with early-bird discounts until March 1.

🔔 Reminder: Abstract submissions close January 15, 23:00 CET—don’t miss your chance to contribute!
Reposted by Carlos Poses
kikollan.llaneras.es
✨ Se publica mi libro en inglés: ‘Think Clearly: Eight Simple Rules to Succeed in the Data Age’ ✨

¡Estoy feliz!

Llega el 23 de enero con #PenguinUK y #Ebury. Y hay más: habrá ediciones en checo, turco, coreano y japonés 👇