Many causal and policy effects of interest are defined by linear functionals
of high-dimensional or non-parametric regression functions.
$\sqrt{n}$-consistent and asymptotically normal estimation of the object of
interest requires debiasing to reduce the effects of regularization and/or
model selection on the object of interest. Debiasing is typically achieved by
adding a correction term to the plug-in estimator of the functional, which
leads to properties such as semi-parametric efficiency, double robustness, and
Neyman orthogonality. We implement an automatic debiasing procedure based on
automatically learning the Riesz representation of the linear functional using
Neural Nets and Random Forests. Our method only relies on black-box evaluation
oracle access to the linear functional and does not require knowledge of its
analytic form. We propose a multitasking Neural Net debiasing method with
stochastic gradient descent minimization of a combined Riesz representer and
regression loss, while sharing representation layers for the two functions. We
also propose a Random Forest method which learns a locally linear
representation of the Riesz function. Even though our method applies to
arbitrary functionals, we experimentally find that it performs well compared to
the state of art neural net based algorithm of Shi et al. (2019) for the case
of the average treatment effect functional. We also evaluate our method on the
problem of estimating average marginal effects with continuous treatments,
using semi-synthetic data of gasoline price changes on gasoline demand.

通过神经网络和随机森林自动学习线性函数的 Riesz 表示，实现自动去偏并提高估计方法的精度，即使是针对高维或非参数回归函数。并且该方法对任意函数都有效，尤其在估计平均治疗效应功能上表现良好。

RieszNet 和 ForestRiesz: 使用神经网络和随机森林实现自动去偏置的机器学习

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with  Neural Nets and Random Forests

We show how to achieve the notion of "multicalibration" from H\'ebert-Johnson
et al. [2018] not just for means, but also for variances and other higher
moments. Informally, it means that we can find regression functions which,
given a data point, can make point predictions not just for the expectation of
its label, but for higher moments of its label distribution as well-and those
predictions match the true distribution quantities when averaged not just over
the population as a whole, but also when averaged over an enormous number of
finely defined subgroups. It yields a principled way to estimate the
uncertainty of predictions on many different subgroups-and to diagnose
potential sources of unfairness in the predictive power of features across
subgroups. As an application, we show that our moment estimates can be used to
derive marginal prediction intervals that are simultaneously valid as averaged
over all of the (sufficiently large) subgroups for which moment
multicalibration has been obtained.

通过多校准算法，我们不仅能预测标签分布的期望值，还能预测更高阶矩，这为在各个子群体上估计不确定性和诊断特征的不公平性提供了一个原则性的方法。

动量多校准方法用于不确定性估计

Moment Multicalibration for Uncertainty Estimation

Machine learning applications often require calibrated predictions, e.g. a
90\% credible interval should contain the true outcome 90\% of the times.
However, typical definitions of calibration only require this to hold on
average, and offer no guarantees on predictions made on individual samples.
Thus, predictions can be systematically over or under confident on certain
subgroups, leading to issues of fairness and potential vulnerabilities. We show
that calibration for individual samples is possible in the regression setup if
the predictions are randomized, i.e. outputting randomized credible intervals.
Randomization removes systematic bias by trading off bias with variance. We
design a training objective to enforce individual calibration and use it to
train randomized regression functions. The resulting models are more calibrated
for arbitrarily chosen subgroups of the data, and can achieve higher utility in
decision making against adversaries that exploit miscalibrated predictions.

通过使用随机化的置信区间实现对每个样本测量的分配，并提高数据子组的校准度和预测效果。