Causality. Why you shouldn't use Bradford Hill criteria!
TLDRThe video discusses the concept of causation and the limitations of Bradford Hill criteria in establishing causal relationships. It argues that while these criteria seem logical, they essentially restate the observed correlation without proving causation. The speaker emphasizes the importance of excluding chance, bias, confounding, reverse causation, and fraud to confidently assert a causal relationship. The video also highlights the University of Limerick's public health master's program, praising its practical approach to preparing graduates for the workforce.
Takeaways
- 🔍 The discussion revolves around the concept of causation and the famous phrase 'correlation doesn't mean causation'.
- 📈 The Bradford Hill criteria are mentioned as a traditional method in public health to discuss causation, but the speaker expresses dissatisfaction with them.
- 👎 The speaker criticizes the Bradford Hill criteria for not being useful in determining the nature of the relationship between exposure and outcome.
- 🔄 The criteria include strength of association, consistency, temporal sequence, dose-response, reversibility, specificity, and plausibility.
- 💡 The speaker argues that most of the criteria restate the correlation without providing evidence of causation, except for plausibility.
- 🚫 Plausibility only rules out relationships that are completely implausible, such as a rooster's crow causing the sun to rise.
- 🤔 A proper approach to understanding causation should exclude chance, bias, confounding, reverse causation, and fraud.
- 🎯 By excluding these factors, one can strengthen the argument that a relationship is causal, provided it is plausible.
- 🏫 The University of Limerick's Master's degree in Public Health is endorsed for its quality and practical preparation for the workplace.
- 🌴 The example of ice cream consumption and shark attacks is used to illustrate confounding, where a third variable (hot summer days) is associated with both.
- ⚠️ The video warns of the dangers of fraud in scientific research, where fabricated relationships can be published and accepted as true.
Q & A
What is the main topic of the transcript?
-The main topic of the transcript is causation and the discussion of causality, specifically the critique of Bradford Hill criteria in public health research.
What does the phrase 'correlation doesn't mean causation' imply?
-The phrase 'correlation doesn't mean causation' implies that just because two events or variables are related or occur together, it does not necessarily mean that one causes the other.
What are the Bradford Hill criteria mentioned in the transcript?
-The Bradford Hill criteria are a set of guidelines used to help determine whether an observed correlation implies causation. They include strength of association, consistency, temporal sequence, dose-response, reversibility, specificity, and plausibility.
Why does the speaker express dislike for the Bradford Hill criteria?
-The speaker dislikes the Bradford Hill criteria because they believe that most of the criteria simply restate the observed correlation without providing evidence about the nature of the relationship, and plausibility only indicates that a relationship is possible, not necessarily causal.
What are the alternatives to causation that the speaker mentions?
-The alternatives to causation mentioned by the speaker are chance, bias, confounding, reverse causation, and fraud.
How can we strengthen the argument for causation between an exposure and an outcome?
-We can strengthen the argument for causation by excluding chance, bias, confounding, reverse causation, and fraud, and if the relationship is plausible, we can conclude that the relationship is likely due to causation.
What is an example of confounding provided in the transcript?
-An example of confounding provided is the correlation between ice cream consumption and shark attacks, which both increase during hot summer days. However, it is not the ice cream consumption that causes shark attacks; rather, they are both associated with the third variable, hot weather.
What is the role of plausibility in determining causation?
-Plausibility is useful in determining causation when the counterfactual seems to be the case. It indicates that the relationship is possible, but it is not a strong indicator of causation on its own.
What is the importance of excluding bias when assessing causation?
-Excluding bias is crucial because bias can systematically distort the results of a study, leading to incorrect conclusions about the relationship between exposures and outcomes.
How does reverse causation differ from the expected direction of causation?
-Reverse causation occurs when the assumed direction of causation is incorrect; instead of exposure A causing outcome B, it is actually outcome B that causes exposure A.
What is the significance of the University of Limerick's Master's degree in Public Health mentioned in the transcript?
-The University of Limerick's Master's degree in Public Health is highlighted as outstanding and designed to prepare graduates for the workplace, ensuring they are ready to contribute effectively in the public health sector.
Outlines
🔍 Understanding Causality and the Critique of Bradford Hill Criteria
This paragraph introduces the concept of causality and the common saying that 'correlation does not imply causation.' It discusses the Bradford Hill criteria, which are often used in public health to assess causation, but the speaker expresses dissatisfaction with these criteria. The speaker argues that the criteria merely restate the observed correlation without providing evidence about the nature of the relationship. The paragraph emphasizes the need to exclude chance, bias, confounding, reverse causation, and fraud to establish a causal relationship, and suggests that plausibility alone is not sufficient to prove causation.
🧐 Exploring Alternatives to Bradford Hill Criteria for Causality Assessment
The second paragraph delves into the limitations of the Bradford Hill criteria by discussing alternative explanations for observed relationships, such as chance, bias, confounding, reverse causation, and fraud. It provides examples to illustrate these concepts, like the correlation between ice cream consumption and shark attacks being due to a confounding factor (hot summer days). The speaker suggests that by excluding these alternatives, one can strengthen the argument for a causal relationship, provided it is plausible. The paragraph concludes with a brief mention of the importance of identifying a mechanism for the proposed causal relationship.
Mindmap
Keywords
💡Causation
💡Correlation
💡Bradford Hill Criteria
💡Plausibility
💡Chance
💡Bias
💡Confounding
💡Reverse Causation
💡Fraud
💡Public Health
Highlights
Today's discussion revolves around causation, causality, and the distinction between correlation and causation.
The Bradford Hill criteria are introduced as a traditional method in public health to discuss causation.
The speaker expresses a dislike for the Bradford Hill criteria and promises to explain why.
Strength of association is described as a strong correlation between the exposure and outcome of interest.
Consistency of finding implies that the same correlation is observed across multiple studies.
Temporal sequence is crucial, where the cause must precede the effect.
Dose-response and reversibility are two sides of the same coin, indicating a relationship's intensity and potential reversal.
Specificity suggests that the exposure is uniquely related to the outcome, rather than being connected to many outcomes.
Plausibility refers to the possibility of the relationship being causal, but it is not a strong indicator of causation.
The speaker argues that the Bradford Hill criteria essentially restate the correlation without providing evidence of causation.
The proper approach to understanding causality involves excluding chance, bias, confounding, reverse causation, and fraud.
By excluding alternative explanations, one can strengthen the argument for a causal relationship.
Chance can lead to observing relationships that do not exist due to sampling error.
Bias can result from flawed study design or incorrect measurement of data.
Confounding occurs when a third variable is associated with both the exposure and outcome, creating a false impression of causation.
Reverse causation is the incorrect assumption of the direction of causality.
Fraud in scientific research can lead to false causal relationships.
The University of Limerick's Master's degree in Public Health is recommended for its practical focus and world-class faculty.
Transcripts
Browse More Related Video
5.0 / 5 (0 votes)
Thanks for rating: