Using Noisy Extractions to Discover Causal Knowledge

TL;DR

This paper presents a probabilistic method that combines noisy information extraction with observational data within a PSL framework to improve causal discovery, especially in complex scenarios like gene regulatory networks.

Contribution

It introduces a novel probabilistic approach that fuses noisy extractions with observational data for causal discovery using PSL, addressing limitations of existing methods.

Findings

Effective in gene regulatory network discovery

Fuses noisy extractions with observational data

Shows promise for causal knowledge discovery

Abstract

Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning. In this work, we study a particular reasoning task, the problem of discovering causal relationships between entities, known as causal discovery. There are two contrasting types of approaches to discovering causal knowledge. One approach attempts to identify causal relationships from text using automatic extraction techniques, while the other approach infers causation from observational data. However, extractions alone are often insufficient to capture complex patterns and full observational data is expensive to obtain. We introduce a probabilistic method for fusing noisy extractions with observational data to discover causal knowledge. We propose a principled approach that uses the probabilistic soft logic (PSL) framework to encode well-studied constraints to recover long-range patterns and consistent predictions, while cheaply acquired extractions provide a proxy for unseen observations. We apply our method gene regulatory networks and show the promise of exploiting KB signals in causal discovery, suggesting a critical, new area of research.