Digging Up Threats to Validity: A Data Marshalling Approach to Sensitivity Analysis
Name
3665601.3669850.pdf
Size
675.35 KB
Format
Adobe PDF
Checksum (MD5)
70b2ac5ae131f052847c779a3e974fab
Author(s)
Zeng, Anna
Cafarella, Mike
Date Issued
June 9, 2024
Publisher
ACM
Citation
Zeng, Anna and Cafarella, Mike. 2024. "Digging Up Threats to Validity: A Data Marshalling Approach to Sensitivity Analysis."
Version
Final published version
Abstract
Causal inference remains a cornerstone for scientific discovery in the natural and social sciences; however, the accuracy of such causal discoveries is susceptible to unobserved confounding bias, the “Achilles heel of most non-experimental studies”. Our principal objective is to bolster the validity of reported causal findings by marshalling pertinent data to corroborate or refute them. In this workshop submission, we describe how data marshalling can turbocharge sensitivity analysis, detail technical challenges, and illustrate a case study as a proof of concept. Our aim in this work is to gather feedback from the audience, gauge interest in solving this open problem relevant to the responsible AI and data management community, and continue iterating on systems that advance the trustworthiness and reproducibility of scientific discoveries.
Description
GUIDE-AI ’24, June 09–15, 2024, Santiago, AA, Chile
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3665601.3669850
