General Control Functions for Causal Effect Estimation from IVs
NIPS 2020, 2020.
Joint independence can be guaranteed via structural treatment process assumptions, like additivity or monotonicity
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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