---
title: Mathematical Causal Graphs
url: https://www.ml-quant.com/papers/ssrn/5284544/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5284544
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5284544
featured: 2025-06-11
citations: unknown
topic: Econometrics & Forecasting
---


# Mathematical Causal Graphs

The paper presents a mathematical framework for studying Causal Graphs with Dynamic Trace GCTD, aiming to pioneer a new research field in discrete mathematics and network theory.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5284544
- Identifier: SSRN 5284544
- Released: 2025-06-06
- First featured: Quant Letter No. 101 (2025-06-11): https://www.ml-quant.com/issues/2025-06-11/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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