# CAREER: Causal Discovery: Methodology Driven by Practice

> **NSF 01003031DB NSF RESEARCH & RELATED ACTIVIT** · Cornell University (NY) · $450,000

## Abstract

Understanding causal mechanisms is a primary goal of scientific inquiry and an increasingly important objective in modern machine learning and artificial intelligence. In contrast to “classical” causal inference, where the goal is to quantify the causal effect of a pre-specified treatment on a pre-specified outcome, this research will focus on causal discovery, which considers entire complex systems and seeks to identify possible causal relationships among many variables simultaneously. Causal discovery has growing relevance in AI and machine learning (ML) applications that require interpretability, robustness, and scientific reasoning from high-dimensional data. For instance, biologists may apply causal discovery to infer causal structure in intracellular networks, neuroscientists may apply causal discovery to recover causal relationships between brain regions, and ML researchers may use these methods to build more reliable predictive models and foundation models that better capture underlying data-generating processes. Despite many exciting theoretical advances and some promising initial applications, important challenges still hinder the widespread use of causal discovery in empirical research and AI systems. This project will develop theory and methods that broaden the use of causal discovery in the empirical sciences and machine learning, enabling researchers across disciplines to apply these methods in a practical, trustworthy, and reliable manner. Most notably, the res

## Key facts

- **NSF award ID:** 2542851
- **Awardee organization:** Cornell University (NY)
- **SAM.gov UEI:** G56PUALJ3KT5
- **PI:** Yu-hsuan S Wang
- **Primary program:** 01003031DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, CAREER-Faculty Erly Career Dev
- **Estimated total:** $450,000
- **Funds obligated:** $303,663
- **Transaction type:** Continuing Grant
- **Period:** 07/01/2026 → 06/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2542851

## Citation

> US National Science Foundation, Award 2542851, CAREER: Causal Discovery: Methodology Driven by Practice. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2542851. Licensed CC0.

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