New study uses causal analysis to demonstrate big reductions in carbon emissions if fewer bovines.
In ten years, methane emissions from all activities if bovine stop would be 80 % of methane emissions from all activities if no intervention.
Results and causation are presented at https://doi.org/10.5281/zenodo.19019693

#carbon #causality #causalInference #causation #confounding #counterFactuals #emissions #GHG #methane #offPolicy #policy #publicPolicy

"Mediators, confounders, colliders – a crash course in causal inference"
by Florian Hartig (2019): https://theoreticalecology.wordpress.com/2019/04/14/mediators-confounders-colliders-a-crash-course-in-causal-inference/

#offPolicy #causality #causalInference #stats #statistics #counterFactuals #probabilities #ML #ecology #confounding

#statstab #471 Give Your Hypotheses Space!

Thoughts: "each hypothesis requires its own model" + "Only interpret the output for your exposure of interest"

#causalinference #modelling #hypothesis #tutorial #confounding #mbias

https://brian-lookabaugh.github.io/website-brianlookabaugh/blog/2025/mutual-adjustment/

#statstab #391 {sensemakr} Sensitivity Analysis Tools for OLS

Thoughts: No unobserved variables is an untestable assumption, but you can quantify the robustness of your ATE.

#R #causalinference #observational #inference #confounding #bias #sensitivity

https://carloscinelli.com/sensemakr/

'Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding', by Jiajing Zheng, Alexander D'Amour, Alexander Franks.

http://jmlr.org/papers/v26/22-0372.html

#confounders #copula #confounding

'Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls', by Erich Kummerfeld, Jaewon Lim, Xu Shi.

http://jmlr.org/papers/v25/22-1062.html

#confounder #causal #confounding

Blasting this #Statistics #CausalInference #Confounding question that's vexing me out there into the aether:

I'm trying to build a Poisson model of a count variable as a function of a set of environmental variables. I have to transform the coefficients into rates by including an offset, as I have different levels of exposure for each measurement. However, I have strong reason to suspect that this offset is also influenced by the same environmental variables.

Am I screwed?

I try to establish the phrase: "You can not have no generative model."

#causality #measurement #selection #confounding #compliance #DAGs