module 12
# Module 12 Flashcards
### Causation and Explanation
Q: What is causation?
A: Causation refers to a relationship where one event (the cause) directly influences another event (the effect).
Q: What role does explanation play in understanding causation?
A: Explanation helps clarify how and why a causal relationship exists, linking causes to their effects logically or empirically.
---
### The Problem with “Seeing” Causation
Q: Why is causation difficult to “see”?
A: Causation cannot be directly observed; it must be inferred from patterns, correlations, and controlled experiments.
Q: What common mistake arises from assuming causation?
A: Confusing correlation with causation, leading to erroneous conclusions about relationships between variables.
---
### Probability and Causation
Q: How does probability relate to causation?
A: Probabilistic reasoning can suggest causal relationships by showing how changes in one variable affect the likelihood of another.
Q: What is probabilistic causation?
A: A concept where a cause increases the probability of its effect but does not guarantee it.
---
### Probabilistic (In)Dependence
Q: What is probabilistic independence?
A: Two variables are independent if the occurrence of one does not affect the probability of the other.
Q: What is probabilistic dependence?
A: A situation where the probability of one event is influenced by the occurrence of another.
---
### Correlation and Causation
Q: What is the difference between correlation and causation?
A: Correlation is a statistical relationship between variables, while causation implies a direct cause-and-effect link.
Q: Why does correlation not imply causation?
A: Correlation can arise from confounders, coincidence, or other indirect relationships.
---
### Common Cause Principle
Q: What is the common cause principle?
A: If two variables are correlated, there is often a common cause influencing both.
Q: Why is identifying a common cause important?
A: It prevents misattribution of causation to one variable when another variable is the true cause.
---
### Confounders
Q: What is a confounder?
A: A variable that influences both the independent and dependent variables, potentially distorting the perceived relationship.
Q: How can confounders be controlled?
A: Through randomization, matching, or statistical adjustments in experiments.
---
### Screening Off
Q: What is the concept of screening off?
A: When a common cause makes two variables independent, the knowledge of one variable does not provide additional information about the other.
Q: Provide an example of screening off.
A: If smoking (common cause) explains both tar in lungs and lung cancer, knowing about tar does not add extra predictive value once smoking is known.
---
### Spurious Correlation
Q: What is spurious correlation?
A: A correlation between variables that arises due to coincidence or a hidden factor, not a causal relationship.
Q: Provide an example of spurious correlation.
A: The correlation between ice cream sales and drowning rates (linked by the hidden variable of hot weather).
---
### Randomized Control Tests (RCTs)
Q: What is an RCT?
A: A study design where participants are randomly assigned to groups to test causal relationships while minimizing bias.
Q: Why are RCTs considered the gold standard in causal inference?
A: They reduce confounders and ensure that observed effects are due to the intervention.
---
### Simpson’s Paradox
Q: What is Simpson’s Paradox?
A: A phenomenon where a trend appears in individual groups but reverses when data is combined.
Q: Provide an example of Simpson’s Paradox.
A: A treatment appears effective in separate gender groups but ineffective when genders are combined due to differing group sizes.
---