AI RESEARCH
Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
arXiv CS.LG
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ArXi:2502.07295v2 Announce Type: replace Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properties -- doubly robustness and fast convergence rates -- remains challenging. A common approach to address this is targeted regularization, which modifies the objective function of NNs.