AI RESEARCH
Data-Driven Influence Functions for Optimization-Based Causal Inference
arXiv CS.LG
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ArXi:2208.13701v5 Announce Type: replace-cross We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inference. We study the case where probability distributions are not known a priori but need to be estimated from data. These estimated distributions lead to empirical Gateaux derivatives, and we study the relationships between empirical, numerical, and analytical Gateaux derivatives.