Causal Inference from the Perspective of Large Models
This presentation by senior AI architect He Gang explores how large language models and LLM‑powered agents can enhance causal inference tasks, detailing model‑assisted analysis, agent‑based inference methods, and multi‑agent simulations to advance causal research.
Speaker: He Gang, senior AI architect at DataCanvas, specializes in frontier AI research including causal inference, large language models (LLM), automated machine learning, and agent‑based modeling, with practical cases in finance and communications.
Talk Title: Causal Inference from the Perspective of Large Models
Outline:
Large models assist causal analysis tasks.
Causal inference based on large‑model agents.
LLM‑powered agents support causal inference research.
Audience Benefits:
Learn how large models can boost causal analysis.
Understand how agent‑based large models perform causal inference.
Discover how multi‑agent simulations with large models aid causal inference algorithm development.
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