Identifiable causal inference with noisy treatment and no side information

arXiv:2306.10614v2 Announce Type: replace
Abstract: In some causal inference scenarios, the treatment variable is measured inaccurately, for instance in epidemiology or econometrics. Failure to correct for the effect of this measurement error can lead to biased causal effect estimates. Previous research has not studied methods that address this issue from a causal viewpoint while allowing for complex nonlinear dependencies and without assuming access to side information. For such a scenario, this study proposes a model that assumes a continuous treatment …

abstract arxiv causal causal inference cs.lg econometrics epidemiology error failure inference information instance issue measurement research stat.me stat.ml treatment type