Bounding the Effects of Continuous Treatments for Hidden Confounders. (arXiv:2204.11206v2 [stat.ME] UPDATED)

Observational studies often seek to infer the causal effect of a treatment
even though both the assigned treatment and the outcome depend on other
confounding variables. An effective strategy for dealing with confounders is to
estimate a propensity model that corrects for the relationship between
covariates and assigned treatment. Unfortunately, the confounding variables
themselves are not always observed, in which case we can only bound the
propensity, and therefore bound the magnitude of causal effects. In many
important cases, like …

arxiv continuous effects