Data-adaptive learning in causal inference for high-dimensional data structures

高维数据结构因果推理中的数据自适应学习

• 批准号: RGPIN-2015-04883
• 负责人: Schnitzer, Mireille
• 金额: $ 1.17万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613232
• 关键词: Data; adaptive; learning; causal; inference

Causal inference is the study of the effects of interventions on an outcome. For instance, we might be interested in evaluating the consequences of a carbon reduction environmental initiative or comparing two different treatment approaches for managing an illness. Mathematically, differences in effects are easy to compare when we perform a randomized experiment. Randomized experiments involve the recruitment of study participants/units who are then randomly allocated one of two interventions. Since the allocation is random, we can compare the results for each intervention group at the end of the study, and any difference between the two groups should be due to the difference in intervention. In particular, due to randomization, the two groups of subjects should look very similar in their characteristics before the intervention is applied. However, in the absence of a randomized experiment, subjects are not randomly assigned to intervention groups, but are differentially exposed to interventions, potentially based on their predispositions. This type of data (called observational since we are not experimentally intervening) is much more complicated to analyze. My research program focuses on the effects of interventions when the only data collected is observational. In this circumstance, I am particularly interested in developing estimators for the effect of interventions that vary over time. One of the complications in evaluating such data is that many factors can influence which subjects experience an intervention at a given time-point, and these factors also affect the study outcome. Special models called "Marginal Structural Models" were developed to represent the effects of interventions in the scenario where the interventions were randomly allocated. We can estimate these models even when data is observational by adjusting for the influential factors. My research program involves developing estimators of these models when there are a very large number of factors that need to be adjusted for, and when interventions can change over time. The estimators that I want to develop will estimate effects of interest very efficiently (i.e. they will better target what we want to estimate than competing estimators) and are robust to mistakes that we make when assuming the structure of certain models (i.e. they will still do a good job even if some of our assumptions are incorrect).

因果推理是研究干预措施对结果的影响。例如，我们可能对评估碳减排环境倡议的后果或比较两种不同的治疗方法来管理疾病感兴趣。从数学上讲，当我们进行随机实验时，效果的差异很容易比较。随机实验涉及招募研究参与者/单位，然后将其随机分配到两种干预措施之一中。由于分配是随机的，我们可以在研究结束时比较每个干预组的结果，两组之间的任何差异都应该是由于干预的差异。特别是，由于随机化，在应用干预之前，两组受试者的特征应该非常相似。然而，在没有随机实验的情况下，受试者并没有被随机分配到干预组，而是有区别地暴露于干预措施，这可能是基于他们的倾向。这种类型的数据（称为观测数据，因为我们没有进行实验干预）分析起来要复杂得多。 我的研究项目集中在干预措施的影响时，收集的唯一数据是观察。在这种情况下，我特别感兴趣的是开发随时间变化的干预措施的效果的估计。评价此类数据的并发症之一是，许多因素会影响哪些受试者在给定时间点接受干预，这些因素也会影响研究结果。开发了称为“边际结构模型”的特殊模型，以表示干预措施随机分配的情景中干预措施的效果。即使数据是观察性的，我们也可以通过调整影响因素来估计这些模型。我的研究计划涉及开发这些模型的估计时，有大量的因素需要调整，当干预措施可以随着时间的推移而改变。我想开发的估计器将非常有效地估计感兴趣的效应（即，它们将比竞争估计器更好地针对我们想要估计的内容），并且对我们在假设某些模型的结构时所犯的错误具有鲁棒性（即，即使我们的一些假设不正确，它们仍然会做得很好）。