Causal Inference in Economics: Master the Art of Identifying Cause and Effect

Question 1

Which of the following is an identification strategy in causal inference that involves assigning treatment randomly to subjects?

Accepted Answer

Randomized controlled trials

Answer

Instrumental variables

Answer

Natural experiments

Answer

Regression discontinuity design

Question 2

In a regression discontinuity design, the treatment assignment is based on:

Accepted Answer

A cutoff point

Answer

Random assignment

Answer

A lottery

Answer

Self-selection

Question 3

The purpose of sensitivity analysis in causal inference is to:

Accepted Answer

Determine the robustness of the causal estimate to changes in assumptions

Answer

Identify potential biases in the analysis

Answer

Increase the precision of the causal estimate

Answer

Reduce the risk of Type I errors

Question 4

Which of the following is a key assumption of the instrumental variables (IV) approach?

Accepted Answer

The instrument is exogenous to the error term

Answer

The instrument is strongly correlated with the endogenous variable

Answer

The sample size is large

Answer

The outcome variable is normally distributed

Question 5

In a difference-in-differences (DiD) analysis, the parallel trend assumption implies that:

Accepted Answer

The treatment group and the control group would have followed similar trends in the absence of treatment

Answer

The treatment effect is constant over time

Answer

The sample size is large

Answer

The outcome variable is normally distributed

Question 6

In a difference-in-differences (DID) design, which group receives the intervention in the post-intervention period?

Accepted Answer

Treatment group

Answer

Control group

Answer

Neither group

Answer

Both groups

Question 7

What is the key identifying assumption in a difference-in-differences (DiD) design?

Accepted Answer

The treatment and control groups are similar in the absence of the intervention.

Answer

The treatment effect is constant over time.

Answer

The treatment effect is large enough to be detected.

Answer

The outcome variable is normally distributed.

Question 8

What is the purpose of a placebo group in a randomized controlled trial?

Accepted Answer

To control for the placebo effect.

Answer

To provide a comparison group.

Answer

To ensure ethical treatment of participants.

Answer

To reduce confounding variables.

Question 9

What is the goal of propensity score matching?

Accepted Answer

To create a comparison group that is similar to the treatment group on observed characteristics.

Answer

To estimate the average treatment effect.

Answer

To test for the presence of confounding variables.

Answer

To reduce the risk of selection bias.

Question 10

What is the key assumption of a regression discontinuity design?

Accepted Answer

The treatment effect is constant over the range of the running variable.

Answer

The treatment assignment is random.

Answer

The running variable is a valid instrument for the treatment.

Answer

The treatment group and control group are similar on all observed characteristics.

Question 11

Instrumental variables (IVs) are primarily used to address which type of bias in causal inference?

Accepted Answer

Confounding

Answer

Selection bias

Answer

Reverse causality

Answer

Measurement error

Question 12

In a difference-in-differences (DiD) design, the parallel trends assumption implies that:

Accepted Answer

The treatment and control groups followed similar trends in the outcome variable prior to the treatment.

Answer

The treatment effect is constant across all individuals in the sample.

Answer

There are no other factors influencing the outcome variable besides the treatment.

Answer

The treatment effect is the same for all time periods.

Question 13

In a regression discontinuity design (RDD), the discontinuity in the treatment assignment variable at the cutoff point is used to:

Accepted Answer

Identify the average treatment effect for compliers.

Answer

Test for the presence of bias.

Answer

Estimate the treatment effect for the entire sample.

Answer

Identify the instrumental variable.

Question 14

In a DiD design, the period before the treatment is implemented is known as the:

Accepted Answer

Pre-period

Answer

Post-period

Answer

Treatment period

Answer

Control period

Question 15

Which of the following is a key assumption of the synthetic control method?

Accepted Answer

The synthetic control group and the treatment group are similar in all relevant pre-treatment characteristics.

Answer

The treatment effect is constant across time.

Answer

There are no other factors influencing the outcome variable besides the treatment.

Answer

The synthetic control group is created from a large set of potential control groups.

Question 16

What fundamental distinction exists between an observational study and an experiment?

Accepted Answer

Observational studies use existing data, while experiments involve manipulating the independent variable.

Answer

Observational studies are inherently less reliable than experiments.

Answer

Experiments are more expensive to conduct than observational studies.

Answer

There is no fundamental distinction between observational studies and experiments.

Question 17

What is the primary purpose of employing a matching estimator?

Accepted Answer

To mitigate bias caused by confounding variables

Answer

To augment the sample size

Answer

To gauge the impact of an intervention

Answer

To determine statistical significance

Question 18

Provide an example of an instrumental variable:

Accepted Answer

Randomly assigned treatment

Answer

Gender

Answer

Age

Answer

Education

Question 19

State the key assumption underlying the regression discontinuity design:

Accepted Answer

The treatment is assigned at a specific threshold of the running variable

Answer

The running variable is randomly assigned

Answer

The treatment has a linear effect on the outcome

Answer

There are no confounding variables

Question 20

Explain the difference between a natural experiment and a quasi-experiment:

Accepted Answer

Natural experiments utilize naturally occurring events, while quasi-experiments involve manipulating the treatment.

Answer

Natural experiments are more reliable than quasi-experiments.

Answer

Natural experiments are less expensive than quasi-experiments.

Answer

There is no difference between natural experiments and quasi-experiments.

Question 21

What is the purpose of a placebo group in a randomized controlled trial?

Accepted Answer

To control for the placebo effect

Answer

To increase the sample size

Answer

To reduce the cost of the study

Answer

To ensure that the results are not biased

Question 22

What is the instrumental variable approach to causal inference?

Accepted Answer

Using an instrumental variable that is correlated with the independent variable but not with the error term

Answer

Using a randomized controlled trial to control for confounding factors

Answer

Matching the treatment and control groups on observed characteristics

Answer

Using regression adjustment to control for confounding factors

Question 23

What is the problem of omitted variable bias?

Accepted Answer

When a confounding variable is not included in a regression model

Answer

When the independent variable is not measured accurately

Answer

When the dependent variable is not measured accurately

Answer

When the sample size is too small

Question 24

What is the role of sensitivity analysis in causal inference?

Accepted Answer

To assess the robustness of the causal effect estimate to different assumptions and specifications

Answer

To increase the sample size

Answer

To reduce the cost of the study

Answer

To ensure that the results are not biased

Question 25

Which ethical principle requires that researchers minimize the potential harm to participants in their studies?

Accepted Answer

Beneficence

Answer

Justice

Answer

Respect for autonomy

Answer

Confidentiality

Question 26

In causal inference, explain the role and methods of sensitivity analysis, emphasizing its significance in assessing the trustworthiness of causal estimates.

Accepted Answer

A method to test the robustness of causal estimates by systematically varying model assumptions and inputs.

Answer

A statistical technique to determine the sample size needed to achieve a desired level of statistical power.

Answer

A statistical method used to model the relationship between a dependent variable and one or more independent variables.

Answer

A procedure to evaluate the plausibility of a hypothesis based on sample data.

Question 27

What is a confounding factor?

Accepted Answer

A variable that is related to both the treatment and the outcome, and can therefore bias the estimated treatment effect.

Answer

A variable that is related to the outcome but not to the treatment.

Answer

A variable that is related to the treatment but not to the outcome.

Question 28

What is the role of instrumental variables (IVs) in causal inference?

Accepted Answer

IVs are used to identify the causal effect of a treatment when there is confounding.

Answer

IVs are used to measure the outcome variable.

Answer

IVs are used to control for selection bias.

Question 29

What is the key difference between a natural experiment and a randomized controlled trial (RCT)?

Accepted Answer

In a natural experiment, the treatment is not randomly assigned, while in an RCT, it is.

Answer

In a natural experiment, the outcome is not measured, while in an RCT, it is.

Answer

In an RCT, the treatment is not randomly assigned, while in a natural experiment, it is.

Question 30

Which of the following is a challenge to causal inference in observational studies?

Accepted Answer

Selection bias can lead to biased estimates of treatment effects.

Answer

It is impossible to identify causal effects in observational studies.

Answer

Observational studies are always more reliable than RCTs.

Question 31

What is the purpose of sensitivity analysis in causal inference?

Accepted Answer

To assess the robustness of causal estimates to violations of assumptions.

Answer

To increase the precision of causal estimates.

Answer

To identify the most important confounding factors.

Question 32

In a randomized controlled trial (RCT), which technique is most effective in controlling for confounding variables?

Accepted Answer

Random assignment of participants to treatment and control groups

Answer

Matching participants on observed characteristics

Answer

Using a regression model to adjust for covariates

Question 33

What is the fundamental principle behind the instrumental variable (IV) approach to causal inference?

Accepted Answer

Using an instrument that affects the treatment variable but not directly the outcome variable

Answer

Examining the relationship between the treatment and outcome variables using regression analysis

Answer

Matching participants on a propensity score

Question 34

What is the primary purpose of a sensitivity analysis in causal inference?

Accepted Answer

Assessing the robustness of the causal estimate to unobserved confounding factors

Answer

Determining the minimum sample size required for statistical significance

Answer

Testing the validity of the exclusion restriction in an instrumental variables model

Question 35

What fundamental distinction exists between a causal effect and a correlation?

Accepted Answer

A causal effect implies a direct relationship between a cause and its effect, whereas a correlation only suggests an association between variables.

Answer

Causal effects are always positive, while correlations can be positive or negative

Answer

Causal effects are always statistically significant, while correlations may not be

Question 36

Which assumption is commonly invoked in causal inference?

Accepted Answer

Stable Unit Treatment Value Assumption (SUTVA)

Answer

Central Limit Theorem

Answer

Law of Large Numbers

Question 37

How do confounding variables differ from mediator variables in causal inference?

Accepted Answer

Confounding variables influence both the treatment and outcome, while mediator variables are affected by the treatment and, in turn, affect the outcome.

Answer

Confounding variables are always observed, whereas mediator variables are always unobserved

Question 38

Which of the following is a potential threat to internal validity?

Accepted Answer

Selection bias

Answer

Confounding variables

Answer

External validity

Question 39

Which of the following is a method for controlling for confounding variables?

Accepted Answer

Matching

Answer

Randomization

Answer

Instrumental variables

Answer

Regression analysis

Question 40

What is the purpose of a natural experiment?

Accepted Answer

To exploit exogenous variation to identify causal effects.

Answer

To control for confounding variables.

Answer

To manipulate an independent variable.

Question 41

Which of the following is a strength of regression discontinuity design?

Accepted Answer

Ability to identify causal effects even in the presence of selection bias.

Answer

Cost-effectiveness

Answer

Generalizability of findings to other contexts.

Question 42

What is the key difference between an observational study and an experimental study?

Accepted Answer

In an observational study, the researcher does not manipulate the independent variable.

Answer

Observational studies are typically more expensive than experimental studies.

Answer

Experimental studies are more likely to produce biased results.

Question 43

Which of the following is a potential benefit of using instrumental variables?

Accepted Answer

Can help to identify causal effects even in the presence of endogeneity.

Answer

Reduces the likelihood of selection bias.

Answer

Increases the precision of the estimated effects.

Question 44

What is the difference between an average treatment effect (ATE) and an average treatment effect on the treated (ATT)?

Accepted Answer

The ATT is the effect of the treatment on those who actually received it, while the ATE is the effect of the treatment on the entire population.

Answer

The ATT is always greater than the ATE.

Question 45

Which of the following is a potential source of bias in a difference-in-differences estimation?

Accepted Answer

Differential time trends between the treatment and control groups.

Answer

Measurement error.

Answer

Random sampling error.

Question 46

What is the role of sensitivity analysis in causal inference?

Accepted Answer

To assess the robustness of the estimated causal effects to alternative assumptions and specifications.

Answer

To identify the most important confounding variables.