Causal Estimation with Functional Confounders
NIPS 2020, 2020.
We develop a new general setting of observational causal effect estimation called estimation with functional confounders where the confounder can be expressed as a function of the data, meaning positivity is violated
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, ...More
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