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#CausalInference

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Ingo Rohlfing

The case for multiple UESDs and an application to migrant deaths in the Mediterranean Sea doi.org/10.1017/psrm.2025.17 #CausalInference Analyzing multiple, comparable unexpected events happening during survey data collection makes a lot of sense to assess patterns. In doing so, one has to follow 1/

Cambridge CoreThe case for multiple UESDs and an application to migrant deaths in the Mediterranean Sea | Political Science Research and Methods | Cambridge CoreThe case for multiple UESDs and an application to migrant deaths in the Mediterranean Sea
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Dr Mircea Zloteanu 🌼🐝

#statstab #307 The C-word, the P-word, and realism in epidemiology

Thoughts: A comment on #306. Causal inference in observational research is a confusing matter. Read both.

#causalinference #observational #research #commentary

link.springer.com/article/10.1

SpringerLinkThe C-word, the P-word, and realism in epidemiology - SyntheseThis paper considers an important recent (May 2018) contribution by Miguel Hernán to the ongoing debate about causal inference in epidemiology. Hernán rejects the idea that there is an in-principle epistemic distinction between the results of randomized controlled trials and observational studies: both produce associations which we may be more or less confident interpreting as causal. However, Hernán maintains that trials have a semantic advantage. Observational studies that seek to estimate causal effect risk issuing meaningless statements instead. The POA proposes a solution to this problem: improved restrictions on the meaningful use of causal language, in particular “causal effect”. This paper argues that new restrictions in fact fail their own standards of meaningfulness. The paper portrays the desire for a restrictive definition of causal language as positivistic, and argues that contemporary epidemiology should be more realistic in its approach to causation. In a realist context, restrictions on meaningfulness based on precision of definition are neither helpful nor necessary. Hernán’s favoured approach to causal language is saved from meaninglessness, along with the approaches he rejects.
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Dr. LabRat

@newsycombinator An illustrative example of collider bias: Location -> Restaurant Success <- Food Quality

I recall that @rlmcelreath used it in one of his lectures… It is surprising how many students of #causalinference (mostly the ones that overtly reject DAGs as a useful source of knowledge representation) miss this effect. I vividly recall how Donald Rubin himself told me that he had never seen any colliders in the real world while answering questions after a lecture at Northwestern. ¯\_(ツ)_/¯

Public
Tom Stafford

Covid-19 Pandemic as a Natural Experiment: The Case of Home Advantage in Sports

journals.sagepub.com/doi/10.11

"The COVID-19 pandemic, with its unparalleled disruptions, offers a unique opportunity to isolate causal effects and test previously impossible hypotheses. Here, we examine the home advantage (HA) in sports—a phenomenon in which teams generally perform better in front of their home fans—and how the pandemic-induced absence of fans offered... natural experiment. "

Establishing a causal relationship requires not only the presence of a factor of interest but also the demonstration that the relationship is absent when the factor is absent. Such ideal conditions are rare, especially in observational studies in which creating control conditions is inherently difficult. The COVID-19 pandemic, with its unparalleled disruptions, offers a unique opportunity to isolate causal effects and test previously impossible hypotheses. Here, we examine the home advantage (HA) in sports—a phenomenon in which teams generally perform better in front of their home fans—and how the pandemic-induced absence of fans offered a fortunate yet systematic change to typical conditions, serving as a natural experiment. Using a structural equation modeling approach and building a mediation model encompassing all relevant HA factors, we quantified the reduction in HA and elucidated the specific mechanisms behind it
#CausalInference
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Dr Mircea Zloteanu 🌼🐝

#statstab #231 Sample Splitting for Valid Powerful Design of Observational Studies

Thoughts: Observational studies are complicated things (more than many will admit). But, maybe there is a way forward (by copying ML!)

#observational #bias #research #methodology #causalinference #causal

arxiv.org/abs/2406.00866

arXiv.orgPlanning for Gold: Sample Splitting for Valid Powerful Design of Observational StudiesObservational studies are valuable tools for inferring causal effects in the absence of controlled experiments. However, these studies may be biased due to the presence of some relevant, unmeasured set of covariates. The design of an observational study has a prominent effect on its sensitivity to hidden biases, and the best design may not be apparent without examining the data. One approach to facilitate a data-inspired design is to split the sample into a planning sample for choosing the design and an analysis sample for making inferences. We devise a powerful and flexible method for selecting outcomes in the planning sample when an unknown number of outcomes are affected by the treatment. We investigate the theoretical properties of our method and conduct extensive simulations that demonstrate pronounced benefits, especially at higher levels of allowance for unmeasured confounding. Finally, we demonstrate our method in an observational study of the multi-dimensional impacts of a devastating flood in Bangladesh.
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