Causal Estimation with Functional Confounders.
Adv Neural Inf Process Syst
; 33: 5115-5125, 2020 Dec.
Article
en En
| MEDLINE
| ID: mdl-33953524
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 confounder and the true outcome function. To estimate effects under these conditions, we develop Level-set Orthogonal Descent Estimation (LODE). Further, we prove error bounds on LODE's effect estimates, evaluate our methods on simulated and real data, and empirically demonstrate the value of EFC.
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01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Adv Neural Inf Process Syst
Año:
2020
Tipo del documento:
Article
Pais de publicación:
Estados Unidos