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Correlation is easy and usually misleading. Causal inference is hard and usually overclaimed. On the benchmark that matters most, the…Continue reading on Towards AI »
Across the social sciences, many studies use cross-sectional designs that reveal associations but are generally unable to support direct causal claims, yet authors of such articles...
by Yunxin Shi, Lulu Pan, Yu Hu, Yongfu Yu, Guoyou Qin Amyloid-Beta 42 (ABeta42) is a key biomarker of cerebral amyloidosis in Alzheimer’s disease, and estimating its causal effect...
This post offers two recommendations and a story. First, the recommendations. • Here’s the book referred to in the title of the post. I highly recommend it! My only regret is that...
I was thinking about the above saying in the context of bad regression discontinuity analyses. Statistical methods can be characterized in terms of how they can go wrong. Some comm...
by Aravind Easwar, Manikandan Narayanan Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interven...
by Yushi Tang, John D. Storey We recently developed a causal inference framework and test—the Transmission Mean Test (TMT)—to identify causal genotype–phenotype relationships in p...
Miha Gazvoda shares this post with the above title and the subtitle, “Using Bayesian multilevel models to correct bias and calibrate uncertainty.” He’s using the chickens model fro...
In reaction to my article with Andy King proposing post-publication review, Dan “Fast and Frugal” Goldstein writes: Your process limits information search, computation, and time so...
Why prediction-driven variable selection misses confounders and how Bayesian Adjustment for Confounding attempts to fix it. The post Why Your Best Predictive Model Gives the Wrong...
From the NYT: Mayor Zohran Mamdani is expected to propose linking Grand Army Plaza with Prospect Park by closing a dangerous stretch of road between them. . . . The plan would effe...
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