Towards causal effect estimation with learned instrument representations
ZNet is a representation learning method that creates instrumental variables directly from observed data when no explicit instruments are known. It learns a feature decomposition into an instrument component and a residual confounder by enforcing moment conditions derived from the structural causal model of IVs. This allows for causal effect estimation using standard IV techniques even without prior knowledge of instruments.