<ul><li>Causal and nonparametric estimands in economics and biostatistics can often be viewed as the mean of a linear functional applied to an unknown outcome regression function.</li><li>Learning the Riesz representer (RR) of the target estimand through automatic debiasing (AD) can be challenging.</li><li>Moment-constrained learning is proposed as a new approach for RR learning, improving the robustness of RR estimates to optimization hyperparameters.</li><li>Numerical experiments on average treatment/derivative effect estimation using semi-synthetic data show improved performance compared to state-of-the-art benchmarks.</li></ul>

Automatic debiasing of neural networks via moment-constrained learning

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