Causal Discovery Should Embrace the Wisdom of the Crowd

TL;DR

This paper advocates for a crowd-based paradigm in causal learning, leveraging collective knowledge, crowdsourcing, and LLMs to improve causal modeling through distributed decision-making.

Contribution

It introduces a framework for crowd-based causal learning, integrating elicitation, aggregation, and optimization, and discusses its potential and challenges.

Findings

Emerging crowd-based approaches enhance causal knowledge collection.

Large language models augment information elicitation in causal learning.

Early research shows promise for decentralized causal modeling.

Abstract

This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and research point to the rise of decentralized and crowd-based approaches within causal modeling, where causal knowledge distributed across many contributors can be systematically elicited and integrated with causal learning workflows. In this paradigm, causal learning becomes a distributed decision-making problem: each participant contributes partial and potentially noisy knowledge, while collective contributions help construct a global causal structure. This direction is enabled by advances in crowdsourcing platforms, expert knowledge elicitation, aggregation techniques, and large language model (LLM)-augmented information acquisition. Its promise is increasingly visible in early research and emerging real-world practices. Building on this momentum, we outline a framework for crowd-based causal learning spanning elicitation, modeling, aggregation, and optimization. We further discuss the opportunities and challenges introduced by this paradigm and call for interdisciplinary collaboration across causal learning, collective intelligence, human-AI interaction, and decision science.