#statstab #505 Beyond Confounders
Thoughts: What makes a good control and a bad control?
#counterfactuals #confounder #DAG #r #modelling #selectionbias #variance #control #causalinference
https://matheusfacure.github.io/python-causality-handbook/07-Beyond-Confounders.html
#statstab #497 On the Statistical Analysis of Experiments
With Manipulation Checks
Thoughts: All psychologists reading this title will panic. Yes, you can't just delete data and assume all is well.
#assumptions #QRPs #estimand #causalinference #ITT #ATE #bias
https://journals.sagepub.com/doi/pdf/10.1177/25152459241297537
"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 #485 Bayesian ANCOVA and the ATE
Thoughts: Still grappling with the implications of using the causal inference approach to randomized experiments. But it's interesting.
#ATE #causalinference #ancova #ANOVA #rstats #estimand #counterfactuals
#statstab #482 Introducing Causion: A web app for playing with DAGs
Thoughts: A very cool app. Let's you see exactly what your assumptions and DGP mean for your causal model.
#causal #causalinference #DAG #DAGs #dgp #tutorial #guide #education #pedagogy
Orthogonal Representation Learning for Estimating Causal Quantities
#statstab #474 {DeclareDesign} Observational : causal
Thoughts: If you want to do causal research with OS there is a lot more to consider than in #473
#design #research #rstats #education #tutorial #pedagogy #DiD #DAGs #causalinference
https://book.declaredesign.org/library/observational-causal.html
Surprising result, nice study design
When "Likes" went private on X there was no detectable change in the number of Likes received on posts by "high reputational risk" accounts
https://arxiv.org/pdf/2601.11140
#CausalInference #DifferenceInDifference #SocialMedia #Research
#statstab #457 Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics
Thoughts: The theoretical framework a researcher uses will affect the causal inference they can make.
#estimand #causalinference #rubin
https://www.annualreviews.org/content/journals/10.1146/annurev-polisci-032015-010015
Although understanding the role of race, ethnicity, and identity is central to political science, methodological debates persist about whether it is possible to estimate the effect of something immutable. At the heart of the debate is an older theoretical question: Is race best understood under an essentialist or constructivist framework? In contrast to the “immutable characteristics” or essentialist approach, we argue that race should be operationalized as a “bundle of sticks” that can be disaggregated into elements. With elements of race, causal claims may be possible using two designs: (a) studies that measure the effect of exposure to a racial cue and (b) studies that exploit within-group variation to measure the effect of some manipulable element. These designs can reconcile scholarship on race and causation and offer a clear framework for future research.
Dr Mircea Zloteanu 🌺🌞🍃
Eric Maugendre about data
Zajey
Joseph A di Paolantonio
Tom Stafford