dimensionality reduction when causal inference is the goal

当因果推理为目标时降维

• 批准号: 2097182
• 金额: --
• 依托单位: London School of Economics and Political Science
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2097182
• 关键词: dimensionality; reduction; when; causal; inference

The vast quantities of data generated in the last decade presents a blessing and a curse for econometrics. It is well documented that 90% of all available data was generated in the last two years alone, thus allowing for a far richer spectrum of regressors, instruments, and controls than previously possible. Conversely, the 'large p small n' paradigm poses numerous challenges for conventional econometrics. At the most fundamental level, when pn the ordinary least squares estimator ceases to be unique. A more interesting problem is posed by spurious collinearity among regressors when p is large, which in turn increases the likelihood of erroneous model selection . As a potential topic for doctoral studies, I am interested in investigating the properties of econometric models for highly dimensional data estimated under sparsity assumptions. While a large body of literature already exists on forecasting in data-rich environments, my interests center specifically around building models for causal inference. At this early stage, I am interested in extending the modelling procedure developed by Chernozhukov et al. (2017); their approach is particularly interesting to me, as causal parameters can be consistently estimated even under erroneous model selection. As a starting point, I note that variable selection via tilting, as proposed by Cho and Fryzlewicz seems particularly amenable to the approach. I would also be interested in extending the approach to data that is correlated in time and space, however I note that significant progress has already been made by Chernozhukov, et al. (2018).

过去十年产生的海量数据对计量经济学来说既是福音，也是诅咒。有充分的文件证明，所有可用数据的90%是在过去两年内产生的，因此允许比以前可能的更丰富的回归变量、工具和控制的频谱。相反，“大p小n”模式给传统计量经济学带来了诸多挑战。在最基本的水平上，当pn时，普通最小二乘估计器不再是唯一的。当p很大时，更有趣的问题是回归变量之间的虚假共线性，这反过来又增加了错误模型选择的可能性。作为博士研究的一个潜在主题，我感兴趣的是研究在稀疏性假设下估计的高维数据的计量经济学模型的性质。虽然已经存在大量关于在数据丰富的环境中进行预测的文献，但我的兴趣主要集中在构建因果推理的模型上。在这个早期阶段，我有兴趣扩展Chernozhukov等人开发的建模程序。(2017)；他们的方法对我特别感兴趣，因为即使在错误的模型选择下，也可以一致地估计因果参数。作为起点，我注意到Cho和Fryzlewicz提出的通过倾斜选择变量似乎特别适合这种方法。我也有兴趣将这种方法扩展到在时间和空间上相关的数据，但我注意到，切尔诺朱科夫等人已经取得了重大进展。(2018)。