Collaborative Research: Causal Structures: Experiments and Machine Learning

协作研究：因果结构：实验和机器学习

• 批准号: 2315665
• 负责人: Guillaume Frechette
• 金额: $ 23.45万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315665&HistoricalAwards=false
• 关键词: Collaborative; Research; Causal; Structures; Experiments

To make decisions, people must rely on their understanding of the relevant environment: what are the causes and outcomes of the various forces at play. In other words, in many settings, including economic ones, people rely on subjective causal models (or narratives) to understand the world. Such models help agents organize and interpret information, allowing them to make forecasts about the future, and providing them with a way to evaluate counterfactuals. The main goal of this research is to take a first step towards understanding how economic agents come to adopt (possibly incorrect) models and how this depends on the information available to them. The researchers will approach this topic from two different perspectives. The first involves a series of experiments that aim to understand how people’s subjective models arise from patterns they identify in data. Some experiments will be conducted in an abstract setting, while others involve natural context. Natural context can trigger preconceptions about how different variables are associated with each other that may help or hinder people from correctly identifying actual patterns in a set of observations. The second approach aims to better understand whether news media plays a role in heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models.A growing literature in economic theory studies ramifications of adopting possibly incorrect subjective models, referring to economic agents relying on such models as ‘misspecified.’ But, for the most part, the literature is silent on how a person comes to adopt a subjective model to begin with, how such a subjective model may depend on the setting, and how it may be shaped by the person’s experiences. In addition, it is an open question under what conditions people adopt subjective models that are consistent with the true data generating process. The goal of this research is to take a first step towards understanding how such misspecifications may arise and how they depend on features of the data-generating process. The researchers will approach the topic from two different perspectives. A first approach involves a series of laboratory experiments to understand how people extract patterns from their observations. The novel experimental design asks subjects to organize different sets of observations (data) with the goal of making predictions in similar situations. The experimental data will let the researchers understand whether the predictions subjects make in each environment are consistent with them using some model that posits specific statistical relationships between different variables. Complemented with ancillary non-choice data that emerges as a by-product of the experimental design, the results will provide insights into how people form models of the world by studying data and how they use these models to make predictions. Experiments will be conducted both with an abstract setting and with context. Understanding how people come to adopt (possibly incorrect) models and how this is impacted by the information available to them is important to determine in what situations they are more vulnerable to being manipulated. Furthermore, it can help us design policies that are effective in correcting beliefs and inducing optimal behavior. The second approach aims to better understand whether news media plays a role in shaping heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models. To do so, the researchers will use an end-to-end trained Machine Learning pipeline that will take text (news articles) as input and identify the main causal statements advanced in this text as output. Documenting the heterogeneous causal models propagated by news outlets is important for understanding why voters with different political affiliation disagree on the optimal response to problems that are accepted by both sides.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了做出决定，人们必须依靠他们对相关环境的理解：各种力量的原因和结果是什么。换句话说，在许多环境中，包括经济环境，人们依靠主观因果模型（或叙事）来理解世界。这些模型帮助代理人组织和解释信息，使他们能够对未来做出预测，并为他们提供一种评估反事实的方法。这项研究的主要目标是迈出第一步，了解经济主体是如何采用（可能不正确的）模型的，以及这如何取决于他们可以获得的信息。研究人员将从两个不同的角度来探讨这个话题。第一个涉及一系列实验，旨在了解人们的主观模型是如何从他们在数据中识别的模式中产生的。一些实验将在抽象的环境中进行，而另一些则涉及自然环境。自然环境可以引发对不同变量如何相互关联的先入之见，这可能有助于或阻碍人们在一组观察中正确识别实际模式。第二种方法旨在更好地理解新闻媒体是否在异质主观模型中发挥作用。目的是研究不同的新闻媒体是否使用不同的因果模型来组织和解释相同的结果。越来越多的经济理论文献研究了采用可能不正确的主观模型的后果，将依赖于这些模型的经济主体称为“错误指定”。“但是，在很大程度上，关于一个人是如何开始采用主观模式的，这种主观模式是如何取决于环境的，以及它是如何被人的经历塑造的，文献都保持沉默。”此外，人们在什么条件下采用与真实数据生成过程一致的主观模型也是一个悬而未决的问题。这项研究的目标是迈出第一步，了解这种错误规范是如何产生的，以及它们如何依赖于数据生成过程的特征。研究人员将从两个不同的角度来研究这个话题。第一种方法包括一系列实验室实验，以了解人们如何从观察中提取模式。这种新颖的实验设计要求受试者组织不同的观察（数据）集，目的是在类似的情况下做出预测。实验数据将让研究人员了解受试者在每个环境中做出的预测是否与他们使用某种模型一致，该模型假定不同变量之间存在特定的统计关系。与作为实验设计副产品而出现的辅助非选择数据相辅相成，结果将为人们如何通过研究数据形成世界模型以及他们如何使用这些模型进行预测提供见解。实验将在抽象背景和情境下进行。了解人们是如何采用（可能不正确的）模型的，以及这是如何受到他们可用的信息的影响的，这对于确定在什么情况下他们更容易被操纵是很重要的。此外，它还可以帮助我们设计出有效纠正信念和诱导最优行为的策略。第二种方法旨在更好地理解新闻媒体是否在塑造异质主观模型方面发挥了作用。目的是研究不同的新闻媒体是否使用不同的因果模型来组织和解释相同的结果。为此，研究人员将使用端到端训练有素的机器学习管道，该管道将文本（新闻文章）作为输入，并识别文本中先进的主要因果陈述作为输出。记录新闻媒体传播的异质因果模型对于理解为什么不同政治派别的选民对双方都接受的问题的最佳反应存在分歧是很重要的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。