AIGC and Causal Inference: Mutual Empowerment and Practical Applications

The presentation introduces the theme "AIGC and Causal Inference Mutual Empowerment" and outlines four main topics: (1) AIGC for structured data synthesis, (2) how synthetic data supports causal inference, (3) using causal inference to aid Agent‑Based Modeling, and (4) an overview of the YLearn causal learning platform.

AIGC, driven by large language models, excels at generating unstructured content but can also be extended to produce structured data. By synthesizing high‑quality tabular data, AIGC provides the missing inputs required for robust causal discovery, effect estimation, and counterfactual analysis, especially in domains where real data are scarce or privacy‑restricted.

Structured data synthesis is crucial for causal inference because it supplies complete feature sets, pre‑defined causal graphs, and counterfactual samples. Synthetic data can be generated via data‑driven methods (GANs, VAEs, Bayesian networks) or process‑driven methods such as Agent‑Based Modeling (ABM), which simulates autonomous agents and their interactions.

ABM offers strong simulation capabilities, emergent behavior analysis, and high interpretability. It can produce counterfactual datasets by running simulations under different parameter configurations, enabling researchers to evaluate causal discovery algorithms, estimate effects with traditional metrics (MSE, RMSE), and explore policy interventions in a controlled environment.

The synergy between ABM and causal inference is illustrated through several use cases: causal discovery validation, causal effect estimation without costly A/B tests, and calibration of ABM parameters using causal effect models. These examples demonstrate how synthetic data bridges the gap between AI generation and causal reasoning.

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