DeepAI AI Chat
Log In Sign Up

Automated Meta-Analysis: A Causal Learning Perspective

by   Lu Cheng, et al.

Meta-analysis is a systematic approach for understanding a phenomenon by analyzing the results of many previously published experimental studies. It is central to deriving conclusions about the summary effect of treatments and interventions in medicine, poverty alleviation, and other applications with social impact. Unfortunately, meta-analysis involves great human effort, rendering a process that is extremely inefficient and vulnerable to human bias. To overcome these issues, we work toward automating meta-analysis with a focus on controlling for risks of bias. In particular, we first extract information from scientific publications written in natural language. From a novel causal learning perspective, we then propose to frame automated meta-analysis – based on the input of the first step – as a multiple-causal-inference problem where the summary effect is obtained through intervention. Built upon existing efforts for automating the initial steps of meta-analysis, the proposed approach achieves the goal of automated meta-analysis and largely reduces the human effort involved. Evaluations on synthetic and semi-synthetic datasets show that this approach can yield promising results.


page 1

page 2

page 3

page 4


Generating Synthetic Text Data to Evaluate Causal Inference Methods

Drawing causal conclusions from observational data requires making assum...

Median bias reduction in random-effects meta-analysis and meta-regression

Random-effects models are frequently used to synthesise information from...

Particulate Matter Exposure and Lung Cancer: A Review of two Meta-Analysis Studies

The current regulatory paradigm is that PM2.5, over time causes lung can...

A Robust Bayesian Meta-Analysis for Estimating the Hubble Constant via Time Delay Cosmography

We propose a Bayesian meta-analysis to infer the current expansion rate ...

Replicability Across Multiple Studies

Meta-analysis is routinely performed in many scientific disciplines. Thi...