General Control Functions for Causal Effect Estimation from IVs

Aahlad Manas Puli
Aahlad Manas Puli

NIPS 2020, 2020.

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Joint independence can be guaranteed via structural treatment process assumptions, like additivity or monotonicity

Abstract:

Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners often use external sources of randomness that only influence the treatment called instrumental variables (IVs). We study variables constructed from treatment and I...More

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