Check your knowledge: Causality in Data Science
Welcome to the Causal Inference Quiz! Test your knowledge on the concepts of causality, treatment effects, challenges in inferring causality, and more. This quiz consists of 10 multiple-choice questions with 4 options each. Choose the best answer and see how much you know about causal inference.
- What is the difference between correlation and causation?
a) Correlation implies causation, while causation does not imply correlation
b) Correlation is the relationship between two variables where they change together, while causation is the relationship between two variables where one variable causes a change in the other variable
c) There is no difference between correlation and causation
d) Causation always implies correlation
Answer: b)
2. What are the three fundamental criteria for establishing causality?
a) Temporal precedence, correlation, and magnitude
b) Correlation, magnitude, and consistency
c) Temporal precedence, correlation, and non-spuriousness
d) Temporal precedence, Correlation, and consistency
Answer: c)
3. What is the difference between a confounder and a mediator?
a) A confounder is a variable that affects both the independent and dependent variables, while a mediator explains the relationship between the independent and dependent variables
b) A confounder explains the relationship between the independent and dependent variables, while a mediator is a variable that affects both the independent and dependent variables
c) There is no difference between a confounder and a mediator
d) A mediator explains the relationship between the independent and dependent variables, while a confounder is a variable that affects both the independent and dependent variables
Answer: a)
4. What is the average treatment effect (ATE)?
a) The difference between the treatment and control groups
b) The difference in outcomes between the treatment and control groups, adjusted for confounding variables
c) The ratio of outcomes between the treated and untreated groups
d) The probability of treatment success
Answer: b)
5. What is the difference between the average treatment effect on the treated (ATT) and the conditional average treatment effect (CATE)?
a) The ATT measures the effect of treatment on those who received it, while the CATE measures the effect of treatment on different subgroups
b) The ATT measures the effect of treatment on different subgroups, while the CATE measures the effect of treatment on those who received it
c) There is no difference between the ATT and CATE
d) ATE is the effect of the treatment on a subgroup of the population, while CATE is the effect of the treatment on the whole population.
Answer: a)
6. What is a randomized control trial (RCT)?
a) A study design where participants are randomly assigned to treatment and control groups
b) A study design where participants self-select into treatment and control groups
c) A study design where the researcher assigns participants to treatment and control groups based on their characteristics
d) The difference between the treatment and control groups
Answer: a)
7. What is the role of instrumental variables in causal inference?
a) To control for confounding variables in observational studies
b) To create a control group in randomized controlled trials
c) To estimate the effect of a treatment on a specific subgroup
d) To identify the causal effect of a treatment in the presence of unobserved confounders
Answer: d)
8. What is the difference between the Average Treatment Effect (ATE) and the Average Treatment Effect on the Treated (ATT)?
a) ATE is the effect of the treatment on the whole population, while ATT is the effect of the treatment on a subgroup of the population.
b) ATE is the effect of the treatment on a subgroup of the population, while ATT is the effect of the treatment on the whole population.
c) ATE and ATT are the same thing. d) ATE and ATT are not related to treatment effects.
d) The ATT measures the effect of treatment on different subgroups, while the ATT measures the effect of treatment on those who received it
Answer: a)
9. What is selection bias?
a) Bias that arises when the sample size is too small.
b) Bias that arises when the sample is not representative of the population.
c) Bias that arises when there are confounding variables in the analysis.
d) Bias that arises when the data is missing.
Answer: b)
10. What is the purpose of propensity score matching?
a) To adjust for confounding variables in observational studies
b) To create a control group in randomized controlled trials
c) To estimate the effect of a treatment on a specific subgroup
d) To control for reverse causality
Answer: a)
Congratulations, you have completed the Causal Inference Quiz! I hope this quiz has deepened your understanding of causal inference and its related concepts. Keep exploring the fascinating world of causality, and remember, correlation does not always equal causation!
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