#confounders — Public Fediverse posts

'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

'A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment', by Robert Hu, Dino Sejdinovic, Robin J. Evans.

http://jmlr.org/papers/v25/21-1409.html

#confounders #causal #inference

.@carl_veller & @gcbias present a theoretical analysis of the influence of #confounders in population- & family-based #GWAS, showing that family-based studies, though more rigorous, still carry subtle issues that arise from confounding. #PLOSBiology https://plos.io/3Qmu2hF

'High-Dimensional Inference for Generalized Linear Models with Hidden Confounding', by Jing Ouyang, Kean Ming Tan, Gongjun Xu.

http://jmlr.org/papers/v24/22-0834.html

#confounders #inferences #debiasing

'High-Dimensional Inference for Generalized Linear Models with Hidden Confounding', by Jing Ouyang, Kean Ming Tan, Gongjun Xu.

http://jmlr.org/papers/v24/22-0834.html

#confounders #inferences #debiasing

'High-Dimensional Inference for Generalized Linear Models with Hidden Confounding', by Jing Ouyang, Kean Ming Tan, Gongjun Xu.

http://jmlr.org/papers/v24/22-0834.html

#confounders #inferences #debiasing

'High-Dimensional Inference for Generalized Linear Models with Hidden Confounding', by Jing Ouyang, Kean Ming Tan, Gongjun Xu.

http://jmlr.org/papers/v24/22-0834.html

#confounders #inferences #debiasing

'High-Dimensional Inference for Generalized Linear Models with Hidden Confounding', by Jing Ouyang, Kean Ming Tan, Gongjun Xu.

http://jmlr.org/papers/v24/22-0834.html

#confounders #inferences #debiasing

'Scalable Computation of Causal Bounds', by Madhumitha Shridharan, Garud Iyengar.

http://jmlr.org/papers/v24/22-1081.html

#causal #confounders #solvers

'The Proximal ID Algorithm', by Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen.

http://jmlr.org/papers/v24/21-0950.html

#causal #unobserved #confounders

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

1️⃣ #Panel data
In panel data, specific units of #observation are surveyed or observed multiple times over time. #examples Students in a class are asked for a weekly self-assessment or the GDP of EU countries is surveyed annually.

2️⃣ Advantages / Disadvantages
#Panel data allows us to analyze the influence of events on a #variable and control for time-constant #confounders. Problematic is the drop of observation units and the influence of past on future surveys.