#confounding — Public Fediverse posts

New study uses causal inference to demonstrate that we would avoid 20 % methane emissions if one commodity was replaced.
I have just published a preprint article at https://doi.org/10.5281/zenodo.19019693

#decoupling #agriculture #beef #cattle #carbon #causality #causalInference #causation #confounding #counterFactuals #emissions #GHG #methane #offPolicy #policy #publicPolicy #vegan #climateChange #GreenhouseForcing #greenhouseEffect

New study uses causal inference to demonstrate that we would avoid 20 % methane emissions if one commodity was replaced.
I have just published a preprint article at https://doi.org/10.5281/zenodo.19019693

#decoupling #agriculture #beef #cattle #carbon #causality #causalInference #causation #confounding #counterFactuals #emissions #GHG #methane #offPolicy #policy #publicPolicy #vegan #climateChange #GreenhouseForcing #greenhouseEffect

New study uses causal inference to demonstrate that we would avoid 20 % methane emissions if one commodity was replaced.
I have just published a preprint article at https://doi.org/10.5281/zenodo.19019693

#decoupling #agriculture #beef #cattle #carbon #causality #causalInference #causation #confounding #counterFactuals #emissions #GHG #methane #offPolicy #policy #publicPolicy #vegan #climateChange #GreenhouseForcing #greenhouseEffect

New study uses causal inference to demonstrate that we would avoid 20 % methane emissions if one commodity was replaced.
I have just published a preprint article at https://doi.org/10.5281/zenodo.19019693

#decoupling #agriculture #beef #cattle #carbon #causality #causalInference #causation #confounding #counterFactuals #emissions #GHG #methane #offPolicy #policy #publicPolicy #vegan #climateChange #GreenhouseForcing #greenhouseEffect

New study uses causal inference to demonstrate that we would avoid 20 % methane emissions if one commodity was replaced.
I have just published a preprint article at https://doi.org/10.5281/zenodo.19019693

#decoupling #agriculture #beef #cattle #carbon #causality #causalInference #causation #confounding #counterFactuals #emissions #GHG #methane #offPolicy #policy #publicPolicy #vegan #climateChange #GreenhouseForcing #greenhouseEffect

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

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

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

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

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

Directed Acyclic Graphs (DAGs) and Regression for Causal Inference by Susan Alber (2022) health.ucdavis.edu/media-resour... #intervention #policy #causality #causalInference #stats #statistics #counterFactuals #probabilities #causation #confounding

Directed Acyclic Graphs (DAGs) and Regression for Causal Inference
by Susan Alber (2022) https://health.ucdavis.edu/media-resources/ctsc/documents/pdfs/directed-acyclic-graphs20220209.pdf

#intervention #policy #causality #causalInference #stats #statistics #counterFactuals #probabilities #causation #confounding #DAG #DAGs #graphs

Directed Acyclic Graphs (DAGs) and Regression for Causal Inference
by Susan Alber (2022) https://health.ucdavis.edu/media-resources/ctsc/documents/pdfs/directed-acyclic-graphs20220209.pdf

#intervention #policy #causality #causalInference #stats #statistics #counterFactuals #probabilities #causation #confounding #DAG #DAGs #graphs

Directed Acyclic Graphs (DAGs) and Regression for Causal Inference
by Susan Alber (2022) https://health.ucdavis.edu/media-resources/ctsc/documents/pdfs/directed-acyclic-graphs20220209.pdf

#intervention #policy #causality #causalInference #stats #statistics #counterFactuals #probabilities #causation #confounding #DAG #DAGs #graphs

Directed Acyclic Graphs (DAGs) and Regression for Causal Inference
by Susan Alber (2022) https://health.ucdavis.edu/media-resources/ctsc/documents/pdfs/directed-acyclic-graphs20220209.pdf

#intervention #policy #causality #causalInference #stats #statistics #counterFactuals #probabilities #causation #confounding #DAG #DAGs #graphs

Directed Acyclic Graphs (DAGs) and Regression for Causal Inference
by Susan Alber (2022) https://health.ucdavis.edu/media-resources/ctsc/documents/pdfs/directed-acyclic-graphs20220209.pdf

#intervention #policy #causality #causalInference #stats #statistics #counterFactuals #probabilities #causation #confounding #DAG #DAGs #graphs

"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

"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

"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

"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

"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

'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

'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

'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

'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

'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

'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

'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

'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

'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?

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?

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?

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?

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

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

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

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

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

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

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

cool example of fixing #confounding - antibiotics and stone edition (no, it ain't any fancy regression or propensity score matching)

https://journals.lww.com/jasn/Fulltext/2023/08000/Outpatient_Antibiotic_Use_is_Not_Associated_with.11.aspx in JASN with a #VisualAbstract from @CorinaT #Epidemiology

cool example of fixing #confounding - antibiotics and stone edition (no, it ain't any fancy regression or propensity score matching)

https://journals.lww.com/jasn/Fulltext/2023/08000/Outpatient_Antibiotic_Use_is_Not_Associated_with.11.aspx in JASN with a #VisualAbstract from @CorinaT #Epidemiology

cool example of fixing #confounding - antibiotics and stone edition (no, it ain't any fancy regression or propensity score matching)

https://journals.lww.com/jasn/Fulltext/2023/08000/Outpatient_Antibiotic_Use_is_Not_Associated_with.11.aspx in JASN with a #VisualAbstract from @CorinaT #Epidemiology

cool example of fixing #confounding - antibiotics and stone edition (no, it ain't any fancy regression or propensity score matching)

https://journals.lww.com/jasn/Fulltext/2023/08000/Outpatient_Antibiotic_Use_is_Not_Associated_with.11.aspx in JASN with a #VisualAbstract from @CorinaT #Epidemiology

cool example of fixing #confounding - antibiotics and stone edition (no, it ain't any fancy regression or propensity score matching)

https://journals.lww.com/jasn/Fulltext/2023/08000/Outpatient_Antibiotic_Use_is_Not_Associated_with.11.aspx in JASN with a #VisualAbstract from @CorinaT #Epidemiology

Nature recently introduced new guidelines for studies engaging with questions of race, ethnicity or gender. Among others, it asks researchers to explain how they controlled for #confounding variables. We agree this is important, especially because we think #bias and #disparity should be understood in #causal terms. In this #LSE impact blog, @LudoWaltman and I discuss some of the challenges around this.

https://blogs.lse.ac.uk/impactofsocialsciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/

Nature recently introduced new guidelines for studies engaging with questions of race, ethnicity or gender. Among others, it asks researchers to explain how they controlled for #confounding variables. We agree this is important, especially because we think #bias and #disparity should be understood in #causal terms. In this #LSE impact blog, @LudoWaltman and I discuss some of the challenges around this.

https://blogs.lse.ac.uk/impactofsocialsciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/

Nature recently introduced new guidelines for studies engaging with questions of race, ethnicity or gender. Among others, it asks researchers to explain how they controlled for #confounding variables. We agree this is important, especially because we think #bias and #disparity should be understood in #causal terms. In this #LSE impact blog, @LudoWaltman and I discuss some of the challenges around this.

https://blogs.lse.ac.uk/impactofsocialsciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/

Nature recently introduced new guidelines for studies engaging with questions of race, ethnicity or gender. Among others, it asks researchers to explain how they controlled for #confounding variables. We agree this is important, especially because we think #bias and #disparity should be understood in #causal terms. In this #LSE impact blog, @LudoWaltman and I discuss some of the challenges around this.

https://blogs.lse.ac.uk/impactofsocialsciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/

Nature recently introduced new guidelines for studies engaging with questions of race, ethnicity or gender. Among others, it asks researchers to explain how they controlled for #confounding variables. We agree this is important, especially because we think #bias and #disparity should be understood in #causal terms. In this #LSE impact blog, @LudoWaltman and I discuss some of the challenges around this.

https://blogs.lse.ac.uk/impactofsocialsciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/