Block 1: X-Centered RDs: Causal strategies: Beyond X and Y

Question 1

Ross’ paper: “Is Democracy Good for the Poor”

Answer 1

– Measures child mortality as a proxy for welfare of the poorest

– Research wants to demonstrate causation: Democracy => Benefit to the poorest

– Based on co-variation

– Non-experimental/observational data

– Panel data research design

– Sample = population

Problem: Systematic bias with regards to missing observations (rich, autocratic states)

Question 2

What is the fundamental problem of causation?

Answer 2

One uses a comparison of factuals to create information about counterfactuals

Question 3

Refined definition of a confounder

Answer 3

Any factor that renders the co-variation of X and Y spurious, making evidence of causality difficult

Question 4

Typology of confounders

Answer 4

Common incidents compound collisions with antecedent, exogenous mechanisms

Graph-based typology:

– common cause,

– incidental,

– compound treatment,

– collider,

– antecedent,

– endogenous,

– mechanism

Question 5

Notation in causal graphs: C

Answer 5

C = Confounder that is measured and conditioned

Thus: Uncondition when found

Question 6

Notation in causal graphs: [C]

Answer 6

[C] = Confounder that is unmeasured and unconditioned

Thus: Condition when found

Question 7

Basic principles of conditioning

Answer 7

Question 8

Typology of Confounders: Common Cause

Answer 8

Has a causal effect on both X and Y Solution: Condition on the common cause confounder, thereby breaking the link

Question 9

Typology of Confounders: Incidental

Answer 9

Affects Y and is correlated with X, but not through any identifiable casual relationship

Question 10

Typology of Confounders: Compound Treatment

Answer 10

Researcher fails to distinguish between a causal factor of theoretical interest and a confounder

Question 11

Typology of Confounders: Mechanism

Answer 11

A conditioned factor is endogenous to X

Question 12

Typology of Confounders: Collider

Answer 12

A conditioned factor is affected by both X and Y

Question 13

Typology of Confounders: Antecedent

Answer 13

A conditioned factor affects Y only thorugh X Solution: Uncondition on Antecedent confounder

Question 14

Typology of Confounders: Endogeneity

Answer 14

Situation where Y affects X

Question 15

Typology of Confounders: Mechanism sub-types

Answer 15

1) Front-door 2) No front-door assumptions

Question 16

Three general sorts of confounders

Answer 16

1) Pre-treatment confounders 2) Post-treatment confounders 3) Pre-/ post treatment confounders in longitudinal studies

Question 17

Types of pre-treatment confounders

Answer 17

– assignment, –selection, – self-selection bias

Question 18

List all the strategies of causal inference beyond X and Y

Answer 18

Instruments help condition mechanisms against rivals and alternative causal reasonings thus making them more causally heterogenuous and robus

– Conditioning on confounders

– Intrumental variables

– Mechanisms

– Alternate outcomes

– Causal heterogeneity

– Rival hypotheses

– Robustness tests

– Causal reasoning

Question 19

Strategies of causal inference beyond X and Y: Conditioning on confounders

Answer 19

This approach conditions factors that would otherwise confound the relationship between X and Y

Question 20

Strategies of causal inference beyond X and Y: Intrumental variables

Answer 20

A good instrument is a variable that: 1) is highly correlated with the treatment variable (X), and 2) has no effect on the outcome (Y) except through the treatment variable (X) (the exclusion restriction)

Question 21

Strategies of causal inference beyond X and Y: Mechanisms and the assumptions thereof

Answer 21

Connection between X and Y that explains the covariational relationship

Front door approach assumptions:

– M is the only pathway between X and Y

– The components of M are isolated and measurable

– Any confounders (C) affecting X do not affect M

Question 22

Strategies of causal inference beyond X and Y: Alternate outcomes

Answer 22

Focuses on variation across outcomes, instead of across groups or time.

• Placebo test*: Investigates alternative outcomes that a confounder should have affected, if an effect is noted the X-Y relationship is spurious
• Unconfounded outcome: T*ry and identify an alternative outcome (Y₂), that correlates with Y₁, but is free from confounders
• Within-unit:* Same group is divided into treatment and control sub-groups. Different outcomes, if independent, show the treatment effect

Question 23

Strategies of causal inference beyond X and Y: Causal heterogeneity

Answer 23

When causal heterogeneity is not stochastic (random) the treatment effect can be measured through moderators (Z)

Assumption: The interaction between X and Z (X*Z) must not be influenced by confounders

Question 24

Strategies of causal inference beyond X and Y: Rival hypotheses

Answer 24

As the name implies, strategy where instead of looking at X, the researcher looks at possible alternative causes (Z) of Y

Logic of elimination: If I cannot find any other explanation for variation in Y, there must be some truth in the X-Y link

Critique: By definition never conclusive

Question 25

Strategies of causal inference beyond X and Y: Robustness tests, definition and purpose

Answer 25

Can be defined as any alteration of a benchmark model that test (qualitatively or quantitatively) the plausibility of key assumptions related to study findings Purpose: Create an estimate of the range of variation possible – If the results are very robust (X=\>Y), our confidence in the finding increases

Question 26

Strategies of causal inference beyond X and Y: Causal reasoning

Answer 26

Is essentially a counterfactual though-experiment, where one thinks through the assumptions of the causal inference and especially the DGP

Question 27

What does conditioning mean?

Answer 27

To include a factor within a statistical model or disaggregate it into its individual parts and then hold it constant when one wants to control the variable

Question 28

What do robustness tests create alterations in?

Answer 28

Alterations in the research design: – Opertionalization of key variables – Sampling – Strategies for measuring causal effects – Estimators – Specifications

Question 29

How do you creaete a good counterfactual? (criteria)

Answer 29

1. Clarity 2. Plausibility of the antecedent 3. Conditional plausibility of the consequent 4. Projectability