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Related Concept Videos

Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
Fundamental Attribution Error01:14

Fundamental Attribution Error

According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:

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Related Experiment Video

Updated: May 28, 2026

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

Selective ignorability assumptions in causal inference.

Marshall M Joffe1, Wei Peter Yang, Harold I Feldman

  • 1University of Pennsylvania School of Medicine, PA, USA.

The International Journal of Biostatistics
|October 5, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces selective ignorability, a new approach to causal inference in observational studies. It allows for more accurate estimation of treatment effects, even with imperfect data, by relaxing strict assumptions.

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Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

Area of Science:

  • Epidemiology
  • Biostatistics
  • Observational Research

Background:

  • Causal inference in observational studies often relies on untestable ignorability assumptions.
  • These assumptions are frequently made for convenience rather than belief, limiting the validity of findings.
  • Existing methods struggle with data where treatment assignment is not fully ignorable.

Purpose of the Study:

  • To introduce and mathematically define selective ignorability assumptions for causal inference.
  • To demonstrate how these assumptions can be integrated with structural nested models for valid inference.
  • To explore the application of selective ignorability in scenarios with selective measurement error or missing data.

Main Methods:

  • Mathematical formulation of selective ignorability assumptions.
  • Application of G-estimation and likelihood-based methods for inference on structural nested models.
  • Analysis of an observational database on hemodialysis patients to estimate erythropoietin's effect on mortality.

Main Results:

  • Selective ignorability provides a framework for valid causal inference when full ignorability does not hold.
  • The proposed methods allow for robust analysis even with non-random missing data.
  • The study successfully estimates the effect of erythropoietin on mortality in hemodialysis patients.

Conclusions:

  • Selective ignorability offers a more realistic and flexible approach to causal inference in observational studies.
  • This methodology enhances the reliability of findings from complex datasets.
  • The approach is applicable to critical health outcomes like mortality in patient populations.