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IntroductionCounterfactuals
ConfoundingCriteria
Research Design:
Causal inference and counterfactuals
Johan A. Elkink
University College Dublin
8 March 2013
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Outline
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Inference
In regression analysis we look at the relationship between (a set of)independent variable(s) and a dependent variable.
Statistical inference is concerned with the question how likely it is toobserve this relationship given the null hypothesis of no relationship(frequentist)
A different question is whether or not we can deduce that the
independent variable is a cause of the dependent one.
(Note: references in these slides can be found in the associated handout.)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Inference
In regression analysis we look at the relationship between (a set of)independent variable(s) and a dependent variable.
Statistical inference is concerned with the question how likely it is toobserve this relationship given the null hypothesis of no relationship(frequentist) or how much we should update our beliefs concerning thisrelationship given our new evidence (Bayesian).
A different question is whether or not we can deduce that the
independent variable is a cause of the dependent one.
(Note: references in these slides can be found in the associated handout.)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Association
association 6= causation
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Association
Given that, say, X and Y are correlation (associated), there arestill many possible causal patterns at play.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Many possible patterns
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Inference
Generally, to make causal inferences from your analysis, additionalassumptions need to be made in addition to the ones already madefor associational or predictive inference.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Outline
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Fundamental problem
Imagine, there are two kinds of people, one group, T = 1, that has acollege degree, and another group, T = 0, that does not. We want tomeasure where a college degree leads to a higher salary, Y .
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Fundamental problem
Imagine, there are two kinds of people, one group, T = 1, that has acollege degree, and another group, T = 0, that does not. We want tomeasure where a college degree leads to a higher salary, Y .
What we would like to know is the difference for any individual i whether
they have a college degree or not: Y Ti=1i
− Y Ti=0i
. However, for every
individual i , we either observe Y Ti=1i
, or we observe Y Ti=0i
– they either
have the degree or they don’t.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
We wish ...
Respondent Degree Y Ti=0i
Y Ti=1i
effect
1 Yes 121 133 +122 Yes 100 109 +93 No 90 92 +24 No 87 88 +15 Yes 143 146 +36 Yes 111 124 +137 No 92 92 08 Yes 95 109 +14
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
We wish ... we have ...
Respondent Degree Y Ti=0i
Y Ti=1i
effect
1 Yes 1332 Yes 1093 No 904 No 875 Yes 1466 Yes 1247 No 928 Yes 109
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Potential outcomes
Potential outcome =
{
Y1i if Ti = 1Y0i if Ti = 0
E.g., Y1i is the salary of individual i had (s)he a college degree,irrespective of whether (s)he actually does.
(Angrist & Pischke 2009, 13-14)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Potential outcomes
Potential outcome =
{
Y1i if Ti = 1Y0i if Ti = 0
E.g., Y1i is the salary of individual i had (s)he a college degree,irrespective of whether (s)he actually does.
Yi = Y0i + (Y1i − Y0i )Ti = Y0i + δTi ,
where δ = Y1i − Y0i is the causal effect.
(Angrist & Pischke 2009, 13-14)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Average treatment effect
Because it is impossible to observe individual treatment effect, we usuallyturn to average treatment effect:
E [δ] = E [Y1i − Y0i ] = E [Y1i ]− E [Y0i ],
which we could naively estimate with
δ̂ = E [Y1i |Ti = 1]− E [Y0i |Ti = 0].
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Average treatment effect
Because it is impossible to observe individual treatment effect, we usuallyturn to average treatment effect:
E [δ] = E [Y1i − Y0i ] = E [Y1i ]− E [Y0i ],
which we could naively estimate with
δ̂ = E [Y1i |Ti = 1]− E [Y0i |Ti = 0].
This assumes that E [Y1i ] reflects the salary for people with a college
degree, irrespective of whether they got one or not, and that E [Y0i ]
reflects the salary without a college degree, irrespective of whether they
got one or not.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Counterfactual causality
By making such assumptions – by looking at the ATE – we aremaking a counterfactual argument. We are making assumptions ofwhat Y1i would have been, had i had a college degree.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Counterfactual causality
By making such assumptions – by looking at the ATE – we aremaking a counterfactual argument. We are making assumptions ofwhat Y1i would have been, had i had a college degree.
This implies that we cannot measure a causal effect, only estimateit.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Counterfactual causality
By making such assumptions – by looking at the ATE – we aremaking a counterfactual argument. We are making assumptions ofwhat Y1i would have been, had i had a college degree.
This implies that we cannot measure a causal effect, only estimateit.
To understand when the ATE assumptions are reasonable, we needto look at the effect of covariates – other variables that relate toY , which we will denote by X.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Treatment effect: abbreviations
ATE Average Treatment Effect E [δ] = E [Y1i − Y0i ]ATT ATE for the Treated E [δT ] = E [Y1i − Y0i |Ti = 1]ATC ATE for the Control (untreated) E [δC ] = E [Y1i − Y0i |Ti = 0]
PATE Population ATE E [Y1i − Y0i ]SATE Sample ATE En[Y1i − Y0i ]LATE Local ATE E [Y1i − Y0i |Xi = x ]CATE Conditional ATE E [Y1i − Y0i |Xi = x ]
and analogously we have LATT, PATT, SATC, etceta. Note that ATE,ATT, ATC implicitly refer to population values.
En[·] is the sample mean, i.e. En[x ] = x̄ = 1n
∑
n
i=1, where the En allowsfor the formulation of conditional and counterfactual means.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Bias in causal inference
Using shorthand E01 = E [Y0i |Ti = 1], etc., and taking π as thepopulation proportion that received the treatment,
E [δ] = πE [δ|Ti = 1] + (1− π)E [δ|Ti = 0]
= π(E11 − E01) + (1− π)(E10 − E00)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Bias in causal inference
Using shorthand E01 = E [Y0i |Ti = 1], etc., and taking π as thepopulation proportion that received the treatment,
E [δ] = πE [δ|Ti = 1] + (1− π)E [δ|Ti = 0]
= π(E11 − E01) + (1− π)(E10 − E00)
can be decomposed into
(E11 − E00) = E [δ] + (E01 − E00) + (1− π){(E11 − E01)− (E10 − E00)}.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Bias in causal inference
Using shorthand E01 = E [Y0i |Ti = 1], etc., and taking π as thepopulation proportion that received the treatment,
E [δ] = πE [δ|Ti = 1] + (1− π)E [δ|Ti = 0]
= π(E11 − E01) + (1− π)(E10 − E00)
can be decomposed into
(E11 − E00) = E [δ] + (E01 − E00) + (1− π){(E11 − E01)− (E10 − E00)}.
(E11 − E00) observed difference in effectE [δ] average treatment effect
(E01 − E00) selection bias(1− π){(E11 − E01)− (E10 − E00)} differential treatment effect bias
(Morgan & Winship 2007
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
SUTVA
The stable unit treatment value assumption “SUTVA is simply thea priori assumption that the value of Y for unit i when exposed totreatment t will be the same no matter what mechanism is used toassign treatment t to unit i and no matter what treatments theother units receive.”
(Rubin (1986: 961), as cited in Morgan & Winship (2007: 37))
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Outline
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Confounding
When studying effect of, say, T on Y , by examining the statisticalassociation between the two variables, we need to ascertain that theobserved effect is not caused by a third variable, say, X.
(Pearl 2000: 182-183)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Confounding
When studying effect of, say, T on Y , by examining the statisticalassociation between the two variables, we need to ascertain that theobserved effect is not caused by a third variable, say, X.
“We can say that T and Y are confounded when there is a third variable
X that influences both T and Y ; such a variable is then called a
confounder of T and Y .”
(Pearl 2000: 182-183)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Confounding
Another way of saying this is that if
E (Y |T ,X ) 6= E (Y |T )
andE (T |X ) 6= E (T ),
X is a confounder of the effect of T on Y .
(Lee 2005: 44)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Confounding
If healthier patients take a drug and sicker patients do not, wecan find an association between drug and recovery even whenthe drug does not work.
If sicker patients take a drug and healthier patients do not, wemight not find an association between drug and recovery evenwhen the drug works.
association 6= causation
The first example is also called a spurious effect (not to beconfused with spurious regression).
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Endogeneity
The situation where cor(X, ε) 6= 0 is called endogeneity.
Endogeneity has three main causes:
Measurement error in X
Simultaneity or reverse causation
Omitted variables
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Confounding
Note that confounding is a causal concept, not an associationalone!
(Pearl 2000)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Confounding
Note that confounding is a causal concept, not an associationalone!
X has to have a causal effect on T and X has to have a causaleffect on Y for there to be an issue.
(Pearl 2000)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Outline
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
When to control?
X affects both T and Y =⇒ control
(Lee 2005: 43-48)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Do control
This is the typical case of a confounding factor, and hence shouldbe eliminated through controlling.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Do control
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
When to control?
X affects both T and Y =⇒ control
T affects Y , which in turn affects X =⇒ do not control
(Lee 2005: 43-48)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Don’t control
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Don’t control
In this case, X is an effect of Y . By controlling for X , you canseverily underestimate the effect of T on Y .
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Don’t control
In this case, X is an effect of Y . By controlling for X , you canseverily underestimate the effect of T on Y .
Imagine that a college degree leads to a better income leads to anicer car. Controlling for the price of the car in estimating theeffect of having a college degree on income might cancel the effect.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
When to control?
X affects both T and Y =⇒ control
T affects Y , which in turn affects X =⇒ do not control
T affects X , which in turn affects Y =⇒ do not control ...
(Lee 2005: 43-48)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Don’t control
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Don’t control
To get the overall effect of T on Y , you want to include the effectthrough X .
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Don’t control
To get the overall effect of T on Y , you want to include the effectthrough X .
E.g. if you want to know the effect of changing the policyregarding smoking in pubs on the amount of smoking in general,you do not care through what mechanism this happened (throughpeer pressure, laziness, etc.), but only about the overall effect.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
When to control?
X affects both T and Y =⇒ control
T affects Y , which in turn affects X =⇒ do not control
T affects X , which in turn affects Y =⇒ do not control ...
... unless you explicitly want only the direct effect
(Lee 2005: 43-48)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Remember the following equation:
β∗ = β + φγ
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Remember the following equation:
β∗ = β + φγ
Sometimes you are interested in β (so control), sometimes in β∗
(so don’t control).
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Example: A scholarship for poorer students might help them to get acollege degree, which in turn might help them to earn more money laterin life.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Example: A scholarship for poorer students might help them to get acollege degree, which in turn might help them to earn more money laterin life. Having a scholarship on your CV, however, might also further yourcareer, independent of the effect of having a college degree.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Example: A scholarship for poorer students might help them to get acollege degree, which in turn might help them to earn more money laterin life. Having a scholarship on your CV, however, might also further yourcareer, independent of the effect of having a college degree.
To see the overall effect of the scholarship, don’t control on having acollege degree.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Example: A scholarship for poorer students might help them to get acollege degree, which in turn might help them to earn more money laterin life. Having a scholarship on your CV, however, might also further yourcareer, independent of the effect of having a college degree.
To see the overall effect of the scholarship, don’t control on having acollege degree.
To see the effect of having a scholarship, independent of the effect of
getting a college degree, do control for college degree.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
When to control?
X affects both T and Y =⇒ control
T affects Y , which in turn affects X =⇒ do not control
T affects X , which in turn affects Y =⇒ do not control ...
... unless you explicitly want only the direct effect
X affects Y , but not T , nor the effect of T on Y
(Lee 2005: 43-48)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
When X affects Y , but not T , there is no confounding issue andthe estimates for the effect of T on Y should not be affected byinclusion of X . However, including X in the model can still help forefficiency.
(Gelman & Hill 2007: 177)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
When to control?
X affects both T and Y =⇒ control
T affects Y , which in turn affects X =⇒ do not control
T affects X , which in turn affects Y =⇒ do not control ...
... unless you explicitly want only the direct effect
X affects Y , but not T , nor the effect of T on Y
X affects Y , not T , but it does affect of effect of T on Y (interaction)
(Lee 2005: 43-48)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Here including the interaction in your model can highlight how theeffect is different for different groups.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Maybe control
Here including the interaction in your model can highlight how theeffect is different for different groups.
Note that it affects the interpretation, but that the estimation ofthe overall ATE is not affected by controlling for X .
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Outline
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
The ideal experiment
To avoid any effect of covariates the ideal is to randomly selectparticipants for your research from the overal population (enablesinference to the population) and to randomly assign the treatmentto these participants (enables causal inference).
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
How to control?
Experiment
Field experiment
Natural experiment
Blocking
Matching
Regression
etc.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Kitchen sink
A typical approach in the quantitative social sciences is to collect anumber of different theories / hypotheses, add them all asvariables to a regression, and see “who wins”. This is the kitchensink approach (or garbage can approach).
If anything, the above discussion should have made clear that todraw causal inferences, a clear distinction of treatment fromcovariates is crucial. In other words: focus your research!
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Kitchen sink
A typical approach in the quantitative social sciences is to collect anumber of different theories / hypotheses, add them all asvariables to a regression, and see “who wins”. This is the kitchensink approach (or garbage can approach).
If anything, the above discussion should have made clear that todraw causal inferences, a clear distinction of treatment fromcovariates is crucial. In other words: focus your research!
(Note that the “garbage can” phrase has also been used to argue against
ignoring nonlinearities (Achen 2005), as opposed to careless specification of the
causal effect.)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Kitchen sink
Another way of putting the issue is that the above is all abouttrying to study the effect of a cause (treatment), rather than thecause of an effect. The latter is perhaps ill-defined and runs intothe “infinite regress of causation”.
(See Gerring (2001, 2012) for an extensive discussion of Y -centered and
X -centered research.)
(Gelman & Hill 2007: 187)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Causal diagrams
The preceding examples underline how it is important to alwaysdraw out the causal diagram and consider carefully how you selectcases and select controls when making causal inferences.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Equifinality
Equifinality refers to the situation where a particular outcomemight come about through different causal paths. No particularpath might show a strong association between independent anddependent variable.
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
When to control?How to control?
Equifinality
Equifinality refers to the situation where a particular outcomemight come about through different causal paths. No particularpath might show a strong association between independent anddependent variable.
Should this be seen as problematic for the counterfactual causalinference framework?
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Outline
1 Introduction
2 Counterfactuals
3 Confounding
When to control?
How to control?
4 Criteria
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
KKV’s 5 rules
1 Construct falsifiable theories
2 Build theories that are internally consistent3 Select dependent variables carefully
No endogenous relationshipEnsure variation in dependent variable
4 Maximize concreteness
5 State theories as encompassing as feasible
(King, Keohane & Verba 1994: 100-114)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Gerring’s criteria
Clarity
Manipulability
Separation
Independence (priority)
Impact
Mechanism
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Discussion points
How do causal inference and prediction relate? (“The proof ofthe pudding is in the eating?”)
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Discussion points
How do causal inference and prediction relate? (“The proof ofthe pudding is in the eating?”)
How does the theory thus far translate to qualitative research?Does it?
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Discussion points
How do causal inference and prediction relate? (“The proof ofthe pudding is in the eating?”)
How does the theory thus far translate to qualitative research?Does it?
Are all causal inferences counterfactual?
Johan A. Elkink counterfactual causal inference
IntroductionCounterfactuals
ConfoundingCriteria
Discussion points
How do causal inference and prediction relate? (“The proof ofthe pudding is in the eating?”)
How does the theory thus far translate to qualitative research?Does it?
Are all causal inferences counterfactual?
How does this all relate to causal mechanisms in the sense ofHedstrom & Swedberg?
Johan A. Elkink counterfactual causal inference