Causal Estimation with Functional Confounders.

ABSTRACT: Causal inference relies on two fundamental assumptions: ignorability and positivity. We study causal inference when the true confounder value can be expressed as a function of the observed data; we call this setting estimation with functional confounders (EFC). In this setting ignorability is satisfied, however positivity is violated, and causal inference is impossible in general. We consider two scenarios where causal effects are estimable. First, we discuss interventions on a part of the treatment called functional interventions and a sufficient condition for effect estimation of these interventions called functional positivity. Second, we develop conditions for nonparametric effect estimation based on the gradient fields of the functional confou

SUBMITTER: Puli A 

PROVIDER: S-EPMC8096536 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature