Data-adaptive causal inference methods for effect modification

用于效果修正的数据自适应因果推理方法

• 批准号: RGPIN-2021-03019
• 负责人: Schnitzer, Mireille
• 金额: $ 1.75万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762042
• 关键词: Data; adaptive; causal; inference; methods

"Causal inference" is the study of cause and effect. For instance, we may be interested in knowing whether a treatment has an impact on lessening the symptoms of a disease. Statistically speaking, we can estimate the impact of treatments on average in a given group of individuals. But it may be that some individuals are more greatly impacted by a treatment than others. Therefore, we may also be interested in evaluating which characteristics of the individuals in the group modify the effects for the individual. This knowledge can guide decision making by helping us identify which individuals will most benefit from a treatment, and which individuals will not obtain a benefit and may even be harmed by a treatment. My research program is focused on the development of statistical methods for identifying individual characteristics that can help predict effects for the individual. The data that I use come from studies where treatments were not randomly assigned to individuals (called "observational studies"). This leads to a major challenge - there are often relationships between treatments that individuals "choose" and their general state. Therefore, I focus on statistical approaches that can robustly correct for these relationships in order to estimate effects. Another focus in my research is the development of methods that can "automatically" discover the characteristics that modify effects in individuals. Essentially, this involves letting an algorithm crunch the data and determine which characteristics are related to different sized effects. My specific contributions involve the development of methods for different data structures and types. For example, specific challenges arise when individuals change their treatments over time or when characteristics can influence effects in complex ways. Because they are generally useful for any investigation of individual effects, the methods I develop can be used in health, economics, policy and engineering research across academia, government, and industry.

“因果推理”是对因果关系的研究。例如，我们可能有兴趣知道一种治疗是否对减轻疾病的症状有影响。从统计学上讲，我们可以估计治疗对特定人群的平均影响。但可能有些人受治疗的影响比其他人更大。因此，我们也可能对评估群体中个体的哪些特征改变了对个体的影响感兴趣。这些知识可以帮助我们确定哪些人将从治疗中受益最大，哪些人将不会受益，甚至可能受到伤害，从而指导决策。我的研究项目集中在发展统计方法，以识别个人特征，帮助预测对个人的影响。我使用的数据来自于治疗方法不是随机分配给个体的研究（称为“观察性研究”）。这就带来了一个重大挑战——个体“选择”的治疗方法与他们的总体状态之间往往存在关联。因此，我专注于统计方法，可以稳健地纠正这些关系，以估计影响。我研究的另一个重点是开发能够“自动”发现改变个体影响的特征的方法。从本质上讲，这涉及到让算法处理数据并确定哪些特征与不同大小的效果有关。我的具体贡献包括为不同的数据结构和类型开发方法。例如，当个体随着时间的推移改变治疗方法，或者当特征以复杂的方式影响效果时，就会出现具体的挑战。因为它们通常对任何个人影响的调查都很有用，所以我开发的方法可以用于学术界、政府和工业界的卫生、经济、政策和工程研究。