Collaborative Research: Causal Structures: Experiments and Machine Learning

要做决定，人们必须依靠对相关环境的了解：各种力量发挥作用的原因和结果是什么。换句话说，在许多环境中，包括经济环境中，人们依靠主观的因果模型(或叙事)来理解世界。这样的模型帮助特工组织和解释信息，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(可能是错误的)模型的，以及这是如何依赖于他们可以获得的信息的。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，而另一些实验则涉及自然环境。自然环境会引发关于不同变量如何相互关联的先入为主的概念，这可能有助于或阻碍人们正确地识别一组观察中的实际模式。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。其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。经济学理论中越来越多的文献研究采用可能不正确的主观模型的后果，指的是经济主体依赖于这样的模型，如“错误说明”。但是，在很大程度上，文献中没有提到一个人是如何开始采用主观模式的，这种主观模式可能如何取决于背景，以及它可能如何被个人的经历塑造。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。第一种方法涉及一系列实验室实验，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使用假设不同变量之间特定统计关系的模型，了解受试者在每个环境中做出的预测是否与他们一致。与作为实验设计副产品出现的辅助非选择数据相辅相成的结果，将为人们提供关于人们如何通过研究数据形成世界模型以及如何使用这些模型进行预测的洞察力。实验将在抽象的环境和背景下进行。了解人们如何采用(可能是不正确的)模型，以及这一点如何受到可获得的信息的影响，对于确定在哪些情况下他们更容易被操纵是重要的。此外，它还可以帮助我们设计在纠正信念和诱导最佳行为方面有效的政策。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。其目的是研究不同的新闻机构是否使用不同的因果模型组织和解释相同的结果。为此，研究人员将使用端到端经过训练的机器学习管道，该管道将以文本(新闻文章)为输入，并将本文中提出的主要因果陈述识别为输出。记录新闻媒体宣传的各种因果模型，对于理解为什么不同政治派别的选民在双方都接受的问题的最佳反应上存在分歧非常重要。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.