Block 1: X-Centered Research- Causal Arguments and Experiments (Lesson 2)

Question 1

Definition of causality

Answer 1

Remember: This is the goal of most x-centered research!

To say that a factor, X, is the cause of an outcome, Y

= a change in X generates a change in Y,

through a mechanism (M)

relative to what Y would otherwise be (counterfactual),

given certain ceteris paribus assumptions

and scope conditions

Question 2

The Causal Diagram

Answer 2

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Question 3

General Criteria of a good causal argument (NOT descriptive)

Answer 3

• MSMCII*
• A manipulable separation mechanism with a clear, independent impact*

Manipulability: Is X manipulable?

Separation: How separable is X relative to Y?

Mechanism: How does X generate Y? (M)

Clarity: Can X and Y be operationalized?

Independence: Is X independent of other causes of Y?

Impact: How much of the variation in Y can X explain? (significance)

Question 4

Moving from argument to analysis

Answer 4

Going from claims to verifying

Question 5

What is the standard theory of causation called in the social sciences? What does it comprise of?

Answer 5

Neyman-Rubin-Holland Theory of Causation

Comprises of a:

– Factual

– Counterfactual

– Antecedent (event)

– Causal effect

Question 6

Formal (mathematical) way of writing a treatment effect on variable “A”

Answer 6

E_A = Y_A(1) – Y_A(0)

Question 7

What is experimental data?

Answer 7

Where you control the data generation process

Question 8

What is observational data?

Answer 8

Where you do NOT control the data generation process (real-world data) Difficult to control for all confounders

Question 9

Issue with experiments?

Answer 9

Internal vs. external validity Might be highly valid internally, but have no external validity Also, experimental situations are often artificial or context-dependent

Question 10

Describe the problem of causation (causal inference) for observational data

Answer 10

We cannot control for common-cause confounders without knowing the data generation process, which can be difficult in real-world scenarios Might create problems of endogeneity

Question 11

Definition of a confounder

Answer 11

Any factor that might interfere with finding causality from covariational evidence => Creates a wrong or spurious X-Y relationship)

Question 12

What is the selectivity problem?

Answer 12

Unless you can control for the selection process, chosen criteria for putting cases into treatment and control groups may influence the outcome in unwanted ways

Question 13

Explain conditional independence

Answer 13

Use control variables to control for the selection process, so the remaining variation in X/Y is the result of the treatment effect

Question 14

Experimental studies and types of these

Answer 14

Study where the researcher controls the data generation process – Laboratory experiments – Field experiments

Question 15

Non-experimental studies and types of these

Answer 15

Researchers do NOT control the data-generating process – Natural experiments – Quasi-experiments – Classic observational studies

Question 16

The Three Hallmarks of an Experiment

Answer 16

– Response of subjects to a ‘treatment’ is compared to the response of other subjects in a ‘control’ group (often no treatment).

– The assignment of subjects to treatment and control groups is random (identical sampling probability).

– Application of the treatment is under the control of the researcher (control for confounders)

Question 17

The strengths of experiments

Answer 17

Explanation:

– Experiments help determine causality by creating a good counterfactual

– Randomization is used to rule out any other explanatory variable

– Experimental control enables researchers to create the difference between treatment and control groups

– With perfect experiments, the control group is therefore extremely comparable to the counterfactual for the treatment group.

Effects:

– High internal validity

Question 18

Why do we not see more experiments in the social sciences?

Answer 18

– Issues pertaining to external validity (whether results are can broadly be replicated and generalized)

– Experiments are best suited to analyze individual behavior

– Some outcomes cannot be replicated through experiments

– Experiments are expensive

– Ethical issues

Question 19

Definition of field experiments

Answer 19

Field experiments try to simulate the conditions under which a causal process occurs, to enhance external validity

Question 20

Difference between laboratory and field experiments

Answer 20

Laboraty experiments:

– Have better control over the experiment

– Create environments that do not exist in reality

– Greater range of variation can be induced

– BUT: Always suffer under questions about external validity

Question 21

Definition of natural experiments

Answer 21

1. Natural experiments are observational (non-experimental) studies 2. Assignment of the subjects to treatment and control groups is “as if” random (no self-selection!) Important: If this assumption doesn’t hold, we cannot speak of a natural experiment!

Question 22

What kind of treatment effect does causal analysis focus on?

Answer 22

Focuses on mean causal effects, i.e. the Average Treatment Effect (ATE), at the population level

Question 23

What is ITE?

Answer 23

Individual Treatment Effect is the impact of the treatment on a single unit compare to its control group

Question 24

What is ATE?

Answer 24

Average Treatment Effect (population level) ATE is the mean impact of a change in X on Y across the population It equals the average of ITE

Question 25

Typology of Treatment Effects

Answer 25

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