As artificial intelligence and machine learning tools become more accessible,
and scientists face new obstacles to data collection (e.g. rising costs,
declining survey response rates), researchers increasingly use predictions from
pre-trained algorithms as outcome variables. Though appealing for financial and
logistical reasons, using standard tools for inference can misrepresent the
association between independent variables and the outcome of interest when the
true, unobserved outcome is replaced by a predicted value. In this paper, we
characterize the statistical challenges inherent to this so-called
``post-prediction inference'' problem and elucidate three potential sources of
error: (i) the relationship between predicted outcomes and their true,
unobserved counterparts, (ii) robustness of the machine learning model to
resampling or uncertainty about the training data, and (iii) appropriately
propagating not just bias but also uncertainty from predictions into the
ultimate inference procedure. We also contrast the framework for
post-prediction inference with classical work spanning several related fields,
including survey sampling, missing data, and semi-supervised learning. This
contrast elucidates the role of design in both classical and modern inference
problems.

以人工智能和机器学习为基础，本文研究了预测后推断问题的统计挑战，包括预测结果与真实结果之间的关系、机器学习模型对训练数据的鲁棒性以及将预测的偏差和不确定性传播到最终推断过程中。同时与传统领域的相关研究进行对比，揭示了设计在经典和现代推断问题中的作用。

我们真的需要数据吗？

Do We Really Even Need Data?

Big Data bring new opportunities to modern society and challenges to data
scientists. On one hand, Big Data hold great promises for discovering subtle
population patterns and heterogeneities that are not possible with small-scale
data. On the other hand, the massive sample size and high dimensionality of Big
Data introduce unique computational and statistical challenges, including
scalability and storage bottleneck, noise accumulation, spurious correlation,
incidental endogeneity, and measurement errors. These challenges are
distinguished and require new computational and statistical paradigm. This
article give overviews on the salient features of Big Data and how these
features impact on paradigm change on statistical and computational methods as
well as computing architectures. We also provide various new perspectives on
the Big Data analysis and computation. In particular, we emphasis on the
viability of the sparsest solution in high-confidence set and point out that
exogeneous assumptions in most statistical methods for Big Data can not be
validated due to incidental endogeneity. They can lead to wrong statistical
inferences and consequently wrong scientific conclusions.

这篇文献讨论了大数据的显著特征及其对统计和计算方法的范式变革产生的影响，强调了高置信度集中最稀疏解的可行性，并指出大多数针对大数据的统计方法中的外生性假设无法被验证，容易导致错误的统计推断和错误的科学结论。