Feb. 13, 2024, 12:27 a.m. | Ugur Yildirim

Towards Data Science - Medium towardsdatascience.com

How to know the unknowable in observational studies


  1. Introduction
  2. Problem Setup
    2.1. Causal Graph
    2.2. Model With and Without Z
    2.3. Strength of Z as a Confounder
  3. Sensitivity Analysis
    3.1. Goal
    3.2. Robustness Value
  4. PySensemakr
  5. Conclusion
  6. Acknowledgements
  7. References

1. Introduction

The specter of unobserved confounding (aka omitted variable bias) is a notorious problem in observational studies. In most observational studies, unless we can reasonably assume that treatment assignment is as-if random as in a natural experiment, we can never …

causal inference confounding editors pick observational-studies sensitivity-analysis

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Engineering Director-Big Data technologies (Hadoop, Spark, Hive, Kafka)

@ Visa | Bengaluru, India

Senior Data Engineer

@ Manulife | Makati City, Manulife Philippines Head Office

GDS Consulting Senior Data Scientist 2

@ EY | Taguig, PH, 1634

IT Data Analyst Team Lead

@ Rosecrance | Rockford, Illinois, United States