Abel.ai

Motivation

Why Causality Matters: Understanding cause-effect mechanisms is central to science, healthcare, economics, and engineering.
Current Challenges:

Steep learning curve of causal discovery and inference methods
Algorithm diversity and complexity
Lack of large-scale deployment and real-world testing

Research Gap: Causal methods remain theoretically mature but practically inaccessible.

System Design

The architecture of Causal-Copilot is modular and LLM-orchestrated, enabling seamless transitions from data input to actionable insights. Users interact with the system in natural language, eliminating the need for coding. The preprocessing pipeline automatically handles data cleaning, schema extraction, and statistical diagnostics.

The algorithm selection engine intelligently filters and ranks candidate methods according to data characteristics and task requirements, configures hyperparameters, and executes workflows. Postprocessing incorporates bootstrap-based confidence estimation and LLM-driven conceptual refinement, ensuring both statistical robustness and semantic validity. Finally, the system generates interpretable outputs, including causal graphs, effect estimations, counterfactual simulations, and structured PDF reports.

Supported Methods

Causal-Copilot integrates a comprehensive library of methods:

Causal Discovery: PC, FCI, GES, FGES, NOTEARS, GOLEM, LiNGAM, PCMCI, DYNOTEARS.
Causal Inference: Double Machine Learning, Doubly Robust Estimation, Instrumental Variables, Matching (PSM, CEM), Counterfactual Estimation.
Auxiliary Tools: SHAP-based feature importance, anomaly detection, and root cause analysis.
Acceleration: CPU parallelization and GPU optimization for large-scale data.

Reference

Xinyue Wang, Kun Zhou, Wenyi Wu, Har Simrat Singh, Fang Nan, Songyao Jin, Aryan Philip, Saloni Patnaik, Hou Zhu, Shivam Singh, Parjanya Prashant, Qian Shen, Biwei Huang. "Causal-copilot: An autonomous causal analysis agent." arXiv preprint arXiv:2504.13263 (2025).

Paper

Open-source Code

Online Demo

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The overall workflow of our Causal-Copilot, with an example of discovering the causal structure in collected advertising related data.

Autonomous Copilot for Expert Causal Reasoning

Expert Causal-Agent

Agents that act with explicit causal reasoning, optimized for complex, high-stales decision-making.

Overview

Causal analysis is essential for moving beyond correlation to uncover mechanisms that drive phenomena in science, medicine, economics, and engineering. However, existing causal discovery and inference methods are notoriously difficult to apply: they require deep statistical knowledge, involve complex algorithmic choices, and often fail to scale in real-world settings.

Causal-Copilot addresses these challenges by introducing a large language model (LLM)-driven autonomous agent for end-to-end causal analysis. It integrates natural language reasoning, automated algorithm orchestration, statistical validation, and interpretability into a unified framework. By combining more than twenty state-of-the-art methods, Causal-Copilot democratizes causal reasoning, making it accessible to non-experts while maintaining methodological rigor.

Causal

Discovery

Actionable Insights

No-Code Reasoning

Result Simulation