Social Media Ideas for Causal Inference

Latest updates for Causal Inference

Fresh curated links around Causal Inference are collected here so marketers can spot useful updates and turn timely ideas into posts faster.

Post angles to try

Share the most useful takeaway for your audience.

Turn one article into a quick practical checklist.

Ask your audience how this shift affects their work.

Turn angles into scheduled posts

Fresh articles and ideas

Recent curated links from global sources. Generate one free draft from any story, then use SocialBu to schedule and refine your content calendar.

statmodeling.stat.columbia.edu /4 weeks ago

I recommend this book on Applied Regression and Causal Inference

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...

Read source

pub.towardsai.net /1 month ago

I built a causal inference engine. The best thing it does is refuse to answer.

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 »

Read source

marginalrevolution.com /2 weeks ago

Overreaching causal language in the social sciences

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...

Read source

journals.plos.org /2 weeks ago

Double shrinkage transfer causal learning: An application to alzheimer’s disease

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...

Read source

journals.plos.org /1 month ago

NLCD: A method to discover nonlinear causal relations among genes

by Aravind Easwar, Manikandan Narayanan Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interven...

Read source

towardsdatascience.com /1 month ago

Why Your Best Predictive Model Gives the Wrong Treatment Effect

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...

Read source

statmodeling.stat.columbia.edu /1 month ago

What gets you is not what you don’t know but what you don’t know you don’t know.

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...

Read source

journals.plos.org /1 month ago

A generalized test of genotype–phenotype causality in population-sampled nuclear families

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...

Read source

statmodeling.stat.columbia.edu /4 days ago

OK, here’s a statistics problem for you, ripped directly from the headlines!

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...

Read source

statmodeling.stat.columbia.edu /1 month ago

Why quantitative understanding of effect sizes matters, even if all you care about is the presence of the effect

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...

Read source

statmodeling.stat.columbia.edu /1 month ago

“Placebo tests deserve a model, not just a glance.”

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...

Read source

statmodeling.stat.columbia.edu /3 weeks ago

Update on a regression discontinuity dispute: some asynchronous collaboration

Anjali Thomas writes: I am writing to share a paper which is a re-examination of my 2018 AJPS article entitled “Targeting Ordinary Voters or Political Elites” which was previously...

Read source

statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: equivalent models, equivalent weights (locally)

Last month we saw that the Times/Siena Poll is now using energy balancing weights (Huling & Mak, 2024). In a toy example, we saw under which outcome models these weighting meth...

Read source

towardsdatascience.com /1 month ago

Building Models in Two Worlds: From Latent Constructs to Behavioral Signals

My PhD models tried to explain why people engage. My industry models predict who will. The statistics barely changed. Everything around them did. The post Building Models in Two Wo...

Read source

towardsdatascience.com /1 week ago

What We Miss About Missing Values

The hidden assumptions behind the data we observe. The post What We Miss About Missing Values appeared first on Towards Data Science.

Read source

conversableeconomist.com /3 weeks ago

The Generalizability Problem, the Natural Field Experiments Answer

There’s a standard problem in the social sciences in moving from the results of a smaller-scale study to larger-scale applicability. Consider a program in a certain school district...

Read source

Turn fresh research into a full content calendar

Use SocialBu to discover ideas, generate post drafts, and schedule them across your social channels.