English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For exposures $X$ like obesity, no precise and unambiguous definition existsfor the hypothetical intervention $do(X = x_0)$. This has raised concerns aboutthe relevance of causal effects estimated from observational studies for suchexposures. Under the framework of structural causal models, we study how theeffect of $do(X = x_0)$ relates to the effect of interventions on causes of$X$. We show that for interventions focusing on causes of $X$ that affect theoutcome through $X$ only, the effect of $do(X = x_0)$ equals the effect of theconsidered intervention. On the other hand, for interventions on causes $W$ of$X$ that affect the outcome not only through $X$, we show that the effect of$do(X = x_0)$ only partly captures the effect of the intervention. Inparticular, under simple causal models (e.g., linear models with nointeraction), the effect of $do(X = x_0)$ can be seen as an indirect effect ofthe intervention on $W$.

Which practical interventions does the do-operator refer to in causal inference? Illustration on the example of obesity and cancer