#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
'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
Pretty good and non technical (meaning no math, other than addition, multiplication, and division) to marginal structural models and time depending #confounding in #epidemiology.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429147/
Pretty good and non technical (meaning no math, other than addition, multiplication, and division) to marginal structural models and time depending #confounding in #epidemiology.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429147/
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.
ICYMI, @ihaque just finished sharing a tootorial on our recent work @ Recursion investigating a widespread source of systematic #confounding in #CRISPR-Cas9 screens we dub "proximity bias."
https://www.biorxiv.org/content/10.1101/2023.04.15.537038v1
We believe that this confounding has implications for most CRISPR-based discovery and may have implications for therapy.
Some sections haven't yet made their way off of the bird site, so take a look here: https://twitter.com/ImranSHaque/status/1650911267530629120
1) An Example:
Does your #shoesize have an effect on your #income? If you look at the c#orrelation between shoe size and income, you will indeed find out: People with bigger feet have on average a higher salary!
But does that really mean that shoe size has an effect on income? No, because: Gender has an effect on both shoe size and income - so it's a #confounding factor in this context!
Trying to understand #USMNT coach Greg Berhalter's thinking is like trying to understand Finnegan's Wake. #confounding #impenetrable #worldcup #football #soccer
