Toward Automated Uncertainty Quantification in Causal Inference

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.