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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...
Microbe-Plant Interactions01:09

Microbe-Plant Interactions

Microbe-plant interactions represent a dynamic spectrum of associations shaped by intricate chemical signaling. These interactions can be neutral, beneficial, or detrimental, and profoundly influence plant physiology, growth, and ecosystem function. The plant microbiome, comprising bacteria, fungi, archaea, protists, and viruses, plays a pivotal role in mediating these effects through surface colonization, internal colonization, or systemic symbiosis.Mutualistic associations, particularly with...
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...
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:
Introduction to Plant Diversity02:22

Introduction to Plant Diversity

From Water to Land

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Related Experiment Video

Updated: May 15, 2026

Inoculation Strategies to Infect Plant Roots with Soil-Borne Microorganisms
08:16

Inoculation Strategies to Infect Plant Roots with Soil-Borne Microorganisms

Published on: March 1, 2022

Causal Inference in Plant Disease Contexts.

Denis A Shah1, Santosh Sanjel2, Paul D Esker2

  • 11Department of Plant Pathology, Kansas State University, Manhattan, Kansas, USA.

Annual Review of Phytopathology
|May 13, 2026
PubMed
Summary

This review introduces causal inference methods for plant pathology research. It explains concepts like potential outcomes and directed acyclic graphs for agricultural researchers to better understand plant disease causes.

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Area of Science:

  • Plant Pathology
  • Agricultural Science
  • Causal Inference

Background:

  • Many plant pathology questions are causal in nature.
  • Formal causal inference frameworks are underutilized in plant pathology.
  • Agricultural researchers need accessible introductions to causal concepts.

Purpose of the Study:

  • To provide an accessible, higher-level introduction to causal inference for agricultural researchers.
  • To highlight the relevance and application of causal inference methods in plant pathology.
  • To encourage the adoption of causal inference in the discipline.

Main Methods:

  • Introduction to the potential outcomes framework for defining counterfactuals.
  • Explanation of directed acyclic graphs for visualizing causal assumptions.
  • Discussion of randomized controlled trials and observational data analysis for causal effect estimation.

Main Results:

  • The review defines key causal inference concepts.
  • It discusses the application of these methods in agriculturally relevant contexts, including interplot interference and heterogeneous treatment effects.
  • Pioneering studies applying causal inference in plant pathology are highlighted.

Conclusions:

  • Causal inference offers a powerful framework for addressing complex questions in plant pathology.
  • The adoption of these methods can enhance the rigor and understanding of plant disease research.
  • Further application of causal inference, especially with observational data, is encouraged.