Skip to main content
SearchLogin or Signup

From Controlled to Undisciplined Data: Estimating Causal Effects in the Era of Data Science Using a Potential Outcome Framework

Published onAug 31, 2021
From Controlled to Undisciplined Data: Estimating Causal Effects in the Era of Data Science Using a Potential Outcome Framework
·
history

You're viewing an older Release (#1) of this Pub.

  • This Release (#1) was created on Jul 23, 2021 ()
  • The latest Release (#2) was created on Aug 31, 2021 ().

Abstract

This paper discusses the fundamental principles of causal inference—the area of statistics that estimates the effect of specific occurrences, treatments, interventions, and exposures on a given outcome from experimental and observational data. We explain the key assumptions required to identify causal effects, and highlight the challenges associated with the use of observational data. We emphasize that experimental thinking is crucial in causal inference. The quality of the data (not necessarily the quantity), the study design, the degree to which the assumptions are met, and the rigor of the statistical analysis allow us to credibly infer causal effects. Although we advocate leveraging the use of big data and the application of machine learning (ML) algorithms for estimating causal effects, they are not a substitute of thoughtful study design. Concepts are illustrated via examples.


Just Accepted - Preview

7/23/21: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

Comments
0
comment

No comments here