#CausalEffect

GetNews.me

@getnews-me.bsky.social

5 months ago

Introducing the Causal-Effect Score for Database Query Attribution

The Causal‑Effect Score measures a tuple’s influence on a query by running it with and without the tuple; for probabilistic DBs it also multiplies by the tuple’s existence probability. getnews.me/introducing-the-causal-e... #causaleffect #databases

Data Elixir

@dataelixir.com

1 year ago

Matching, missing data, a quasi-experiment, and causal inference--Oh my! | A. Solomon Kurz I'm finally dipping my does into causal inference for quasi-experiments, and my first use case has missing data. In this post we practice propensity score matching with multiply-imputed data sets, and...

Diving into causal analysis with non-randomized groups? Learn how matching and imputation in R can tackle missing data challenges. Perfect for those looking to refine their statistical toolkit!

solomonkurz.netlify.app/blog/2025-02...

#CausalEffect #rstats #DataScience

News Medical

@newsmedical.bsky.social

1 year ago

Mental well-being crucial for healthy aging, new study reveals A study in Nature Human Behavior found that mental well-being has a causal effect on healthy aging, independent of socioeconomic status, with better mental health linked to improved aging outcomes.

Mental well-being crucial for healthy aging, new study reveals 🧠🌿🏃‍♂️ www.news-medical.net/news/2024061... #MentalWellBeing #HealthyAging #HumanBehavior #Aging #MentalHealth #GeneticStudy #LifeExpectancy #HealthSpan #PositiveAging #CausalEffect @natureportfolio.bsky.social

Dennis Alexis Valin Dittrich

@davdittrich.economicscience.net

1 year ago

How statistical challenges … produce unreplicable #science

https://osf.io/preprints/psyarxiv/ekmdf

"…when estimating a #causalEffect from data with a #multilevel structure, it is necessary to allow both the intercepts & effects to vary along each grouping structure. In practice, this is … 1/2

Dr Mircea Zloteanu 🌺🌞🍃

@mzloteanu.bsky.social

2 years ago

Both estimands could be termed the effect of a criminal record on the probability of a callback among Black and White applicants, yet the two are quite different. A design targeting causal interaction (Pager 2003) would randomly assign units (applicant–application pairs) to a cell of the 2 × 2 table that combines all values of both treatments. A design targeting effect heterogeneity would take applications in the real-world distribution for each subgroup and estimate the outcome they would realize if they signaled or did not signal a criminal record. Both estimands are of substantive interest.

I often see researchers confuse the effects they can measure in experiments vs observational studies. This fig from doi.org/10.1177/0003... is my go-to when explaining this:

#stats #causaleffect #experiments #DAGs #research