New paper and software alert! arxiv.org/abs/2408.14620 Interested in modern mediation analysis methods with machine learning and multivariate mediators? Take a look at this joint work with Richard Liu, @nickWillyamz , and Kara Rudolph Short 🧵...

Reporting effect sizes without causal effects is like reporting the height of an imaginary friend — we distract ourselves from the fiction.

do you guys know that you can *design* experiments with TMLE/DML/etc. in mind for the analysis? Here's the power calculation. Smaller sample size for free if you can guess how much your R2 will go down with ML adjustment. degruyter.com/document/doi/1…

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Okay this is fun; a paper by @RmusciPHD and me is used as a citation for the definition of a causal effect in the new NIH/FDA glossary of terms for clinical research! fda.gov/media/178477/d…

Causal Directed Acyclic Graphs (DAGs) are like chainsaws: used well, they slice through heavy tasks; used poorly, and you might lose a limb. Here's the first of four tutorials in Evolutionary Human Sciences for human scientists new to causal inference: cambridge.org/core/journals/… Tips: -

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Using three waves of national panel data, we estimate well-defined causal effects of religious service attendance on charitable giving among New Zealanders and also include measures of help received: journals.sagepub.com/doi/10.1177/00…

1/ That "immortal time" is so frequent in survival analyses for #causalinference is fascinating. Because "immortal time" doesn't exist in the data, *we* create it when misanalyzing data. Our paper pubmed.ncbi.nlm.nih.gov/39494894/ summarizes why immortal time arises & how to prevent it.

I was often asked by practitioners about power calculations for causal inference with observational data, a hard problem with little leads. Finally had a clean solution, thanks to my spectacular student Bo Liu. Here it is: arxiv.org/abs/2501.11181 cran.r-project.org/web/packages/P…

London is famous for its garden squares, laid out by private developers in the eighteenth and nineteenth centuries. Why did they do this? A short thread.

I asked the AI for the least inspiring inspirational poster and I weirdly like it

This is 👍

Please consider contributing an entry or using this poll as a tool for cross-cultural research. All DRH data is grounded in space and time and tagged with language, religious tradition and other data, enabling large-scale analysis in a way that was never before possible.

This looks 🤩