Quant AI and trading AI skills for quant agents and AI trading agents: causal market analysis, investment research, trading strategy discovery, alpha discovery, backtesting, and validation with Abel.

A meticulously curated collection of cutting-edge research, frameworks, and methodologies in Causal Artificial Intelligence.

Engineering-grade causal audit infrastructure for AI agents

CML (Causal Memory Layer) — a foundational memory layer for recording reasons, permissions, and responsibility behind actions, not just events or results. Enables systems in AI, fintech, security, and distributed computing to preserve meaning and causal accountability across time, independent of execution or transport.

Official code for "Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes" EMBC 2025.

Weighted doubly robust learning for uplift modeling

C-DAG: replayable causal audit traces for high-risk financial AI decisions.

Causal inference methods for -omics research

Official reproducibility experiments of the Thesis "Large Causal Models for Temporal Causal Discovery" at the University of Crete, Computer Science Department. Defended at November 12th, 2025.

A five-layer causal-neuro-symbolic framework for machine fault diagnosis. Independently verifies neural predictions against machine physics; domain-agnostic via pluggable providers.

Causal Alignment Explorer is a visual tool to map causal relationships and test "what-if" intervention scenarios using data-driven Directed Acyclic Graphs (DAGs).

Causal memory layer for AI agents. Prevents recurring failures by building a graph of action-outcome chains.

CAK: typed semantic control layer for AI-agent behavior, learning, replay, governance, portability, and cost control.

AI often 'cheats' to score 100%. This Explainable AI (XAI) project reverse-engineers Neuro-Symbolic models via Causal Abstraction Theory to expose hidden reasoning shortcuts and evaluate architectural fixes for truly trustworthy logic.

See what your code actually means. In 3D. Claude Opus 4.7 hackathon entry.

Causal inference project using DoWhy to isolate the true marketing lift of bank contact methods. Applies Propensity Score Stratification to remove selection bias from raw campaign data and delivers an interactive ROI simulator for budget decision-making.

Alternative approaches to AI

A simple way to visualize discrepancies between causal matrices of true and predicted directed acyclic graph

PRD-AGI 2.0.2 – 14 causal AI applications for healthcare, finance, law, education, cybersecurity, mental health, and agriculture.

My Master's dissertation, Large Causal Models for Temporal Causal Discovery. Defended at November 12th, 2025.