AIGC and Causal Inference: Mutual Empowerment and Applications with YLearn

The presentation introduces the theme "AIGC and Causal Inference: Mutual Empowerment" and outlines four main sections: (1) AIGC for structured data synthesis, (2) how synthetic data aids causal inference, (3) leveraging causal inference to improve agent‑based modeling, and (4) an overview of the YLearn causal learning platform.

AIGC, driven by large language models, has excelled at generating unstructured content. By extending its capabilities to structured data synthesis, AIGC can produce high‑quality synthetic datasets that overcome data scarcity, privacy constraints, and cost limitations in domains such as finance, healthcare, and computer vision.

Synthetic data, generated via data‑driven methods (GANs, VAEs, Bayesian networks) or process‑driven methods (agent‑based modeling, discrete‑event simulation, Monte Carlo), can supplement real data for causal discovery, effect estimation, and counterfactual analysis. Agent‑Based Modeling (ABM) serves as a bridge, offering strong simulation fidelity, emergent behavior analysis, and complete feature capture.

ABM provides three key advantages for causal research: (1) access to counterfactual data, (2) comprehensive feature sets, and (3) controllable simulation environments. These properties enable more reliable evaluation of causal discovery algorithms, causal effect metrics (e.g., AUUC, Qini, RLoss), and support both macro‑level policy analysis and micro‑level intervention studies.

The YLearn framework is introduced as an all‑in‑one causal inference toolkit. It supports causal discovery, graph construction, effect estimation (Meta Learner, Causal Forest, etc.), policy learning, interpretation, and counterfactual prediction through a unified why interface, simplifying workflow for researchers and practitioners.

Overall, the article demonstrates how AIGC‑generated synthetic data and ABM can mutually reinforce causal inference research, and how YLearn operationalizes these concepts for practical AI development.