Toward Automated Uncertainty Quantification in Causal Inference

在研究因果关系或根据数据做出重要决策时，研究人员和决策者经常会遇到影响其结论可靠性和可信度的不确定性。无论是在科学实验、政策制定还是设计机器学习系统中，理解和量化这些不确定性对于做出明智的选择至关重要。为此，该项目旨在开发能够有效解决因果推理中不确定性量化挑战的算法。特别是，目前许多量化因果关系不确定性的方法依赖于复杂的数学技术，可能与现实世界的情况不太一致。该项目旨在通过设计理论合理且实际适用于各种情况的算法来改变这种情况。本项目也为研究生提供了研究训练的机会。从技术上讲，该项目将侧重于因果推理中的不确定性，这些不确定性来自两个来源：不确定的因果图和有限的可用数据信息，这两个来源对因果结论和下游决策都有重大影响。为了应对这些挑战，该项目将开发算法，提供灵活和统计有效的不确定性估计，最大限度地减少对特定因果问题的依赖。利用算法稳定性和私有数据分析技术的最新进展，即使因果图不确定或从数据中学习，也将构建因果估计的置信区间。此外，这些置信区间将与可用的领域知识相结合，以进一步量化由有限的领域知识和数据的识别能力引起的不确定性。综上所述，本项目将促进不同不确定性的整合，最终在因果推理中实现更可靠和自动化的不确定性量化。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。

When studying cause-and-effect relationships or making important decisions based on data, researchers and decision-makers often encounter uncertainties that can impact the reliability and trustworthiness of their conclusions. Understanding and quantifying these uncertainties is crucial for making informed choices, whether in scientific experiments, policy-making, or designing machine learning systems. To this end, the project aims to develop algorithms that can effectively address the challenge of uncertainty quantification in causal inference. In particular, many current approaches to quantifying uncertainty in causal relationships rely on sophisticated mathematical techniques that may not align well with real-world scenarios. This project seeks to change the situation by designing algorithms that are both theoretically sound and practically applicable across a wide range of situations. This project also provides research training opportunities for graduate students. Technically, the project will focus on uncertainties in causal inference arising from two sources: uncertain causal graphs and limited informativeness of available data, both of which have significant implications for causal conclusions and downstream decision-making. To tackle these challenges, this project will develop algorithms that provide flexible and statistically valid uncertainty estimates, minimizing their dependence on specific causal problems. Leveraging recent advancements in algorithmic stability and private data analysis techniques, confidence intervals for causal estimates will be constructed, even when the causal graph is uncertain or learned from data. Additionally, these confidence intervals will be integrated with the available domain knowledge to further quantify the uncertainty arising from limited domain knowledge and the identification power of the data. Taken together, this project will facilitate the integration of different uncertainties, ultimately leading to more reliable and automated uncertainty quantification in causal inference.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.