Ch 11 Slides.doc. Introduction to econometric modeling

11-1
Experiments and Quasi-Experiments
(SW Chapter 11)
Why study experiments?
 Ideal randomized controlled experiments provide a
benchmark for assessing observational studies.
 Actual experiments are rare ($$$) but influential.
 Experiments can solve the threats to internal validity
of observational studies, but they have their own
threats to internal validity.
 Thinking about experiments helps us to understand
quasi-experiments, or “natural experiments,” in which
there some variation is “as if” randomly assigned.

11-2
Terminology: experiments and quasi-experiments
 An experiment is designed and implemented
consciously by human researchers. An experiment
entails conscious use of a treatment and control group
with random assignment (e.g. clinical trials of a drug)
 A quasi-experiment or natural experiment has a
source of randomization that is “as if” randomly
assigned, but this variation was not part of a conscious
randomized treatment and control design.
 Program evaluation is the field of statistics aimed at
evaluating the effect of a program or policy, for
example, an ad campaign to cut smoking.

11-3
Different types of experiments: three examples
 Clinical drug trial: does a proposed drug lower
cholesterol?
oY = cholesterol level
oX = treatment or control group (or dose of drug)
 Job training program (Job Training Partnership Act)
oY = has a job, or not (or Y = wage income)
oX = went through experimental program, or not
 Class size effect (Tennessee class size experiment)
oY = test score (Stanford Achievement Test)
oX = class size treatment group (regular, regular +
aide, small)

11-4
Our treatment of experiments: brief outline
 Why (precisely) do ideal randomized controlled
experiments provide estimates of causal effects?
 What are the main threats to the validity (internal and
external) of actual experiments – that is, experiments
actually conducted with human subjects?
 Flaws in actual experiments can result in X and u being
correlated (threats to internal validity).
 Some of these threats can be addressed using the
regression estimation methods we have used so far:
multiple regression, panel data, IV regression.

11-5
Idealized Experiments and Causal Effects
(SW Section 11.1)
 An ideal randomized controlled experiment randomly
assigns subjects to treatment and control groups.
 More generally, the treatment level X is randomly
assigned:
Yi = 0 + 1Xi + ui
 If X is randomly assigned (for example by computer)
then u and X are independently distributed and E(ui|Xi)
= 0, so OLS yields an unbiased estimator of 1.
 The causal effect is the population value of 1 in an
ideal randomized controlled experiment

11-6
Estimation of causal effects in an ideal randomized
controlled experiment
 Random assignment of X implies that E(ui|Xi) = 0.
 Thus the OLS estimator 1
ˆ
 is unbiased.
 When the treatment is binary, 1
ˆ
 is just the difference
in mean outcome (Y) in the treatment vs. control
group ( treated
Y – control
Y ).
 This differences in means is sometimes called the
differences estimator.

11-7
Potential Problems with Experiments in Practice
(SW Section 11.2)
Threats to Internal Validity
1. Failure to randomize (or imperfect randomization)
 for example, openings in job treatment program are
filled on first-come, first-serve basis; latecomers are
controls
 result is correlation between X and u

11-8
Threats to internal validity, ctd.
2. Failure to follow treatment protocol (or “partial
compliance”)
 some controls get the treatment
 some “treated” get controls
 “errors-in-variables” bias: corr(X,u) 0
 Attrition (some subjects drop out)
 suppose the controls who get jobs move out of town;
then corr(X,u) 0

11-9
Threats to internal validity, ctd.
3. Experimental effects
 experimenter bias (conscious or subconscious):
treatment X is associated with “extra effort” or
“extra care,” so corr(X,u) 0
 subject behavior might be affected by being in an
experiment, so corr(X,u) 0 (Hawthorne effect)
Just as in regression analysis with observational data,
threats to the internal validity of regression with
experimental data implies that corr(X,u) 0 so OLS
(the differences estimator) is biased.
George Elton Mayo and the Hawthorne Experiment

11-10
Subjects in the Hawthorne plant experiments, 1924 – 1932

11-11
Threats to External Validity
1. Nonrepresentative sample
2. Nonrepresentative “treatment” (that is, program or
policy)
3. General equilibrium effects (effect of a program can
depend on its scale; admissions counseling )
4. Treatment v. eligibility effects (which is it you want
to measure: effect on those who take the program, or
the effect on those are eligible)

11-12
Regression Estimators of Causal Effects Using
Experimental Data
(SW Section 11.3)
 Focus on the case that X is binary (treatment/control).
 Often you observe subject characteristics, W1i,…,Wri.
 Extensions of the differences estimator:
ocan improve efficiency (reduce standard errors)
ocan eliminate bias that arises when:
 treatment and control groups differ
 there is “conditional randomization”
 there is partial compliance
 These extensions involve methods we have already
seen – multiple regression, panel data, IV regression

11-13
Estimators of the Treatment Effect 1 using
Experimental Data (X = 1 if treated, 0 if control)
Dep.
vble
Ind.
vble(s)
method
differences Y X OLS
differences-in-
differences
Y =
Yafter
– Ybefore
X OLS adjusts for initial
differences between
treatment and control
groups
differences with
add’l regressors
Y X,W1,
…,Wn
OLS controls for
additional subject
characteristics W

11-14
Estimators with experimental data, ctd.
Dep.
vble
Ind.
vble(s)
method
differences-in-
differences with
add’l regressors
Y =
Yafter
– Ybefore
X,W1,
…,Wn
OLS adjusts for group
differences + controls
for subject char’s W
Instrumental
variables
Y X TSLS Z = initial random
assignment;
eliminates bias from
partial compliance
 TSLS with Z = initial random assignment also can be
applied to the differences-in-differences estimator and
the estimators with additional regressors (W’s)

11-15
The differences-in-differences estimator
 Suppose the treatment and control groups differ
systematically; maybe the control group is healthier
(wealthier; better educated; etc.)
 Then X is correlated with u, and the differences
estimator is biased.
 The differences-in-differences estimator adjusts for pre-
experimental differences by subtracting off each
subject’s pre-experimental value of Y
o before
i
Y = value of Y for subject i before the expt
o after
i
Y = value of Y for subject i after the expt
oYi = after
i
Y – before
i
Y = change over course of expt

11-16
1
ˆdiffs in diffs
  
= ( ,
treat after
Y – ,
treat before
Y ) – ( ,
control after
Y – ,
control before
Y )

11-17
The differences-in-differences estimator, ctd.
(1) “Differences” formulation:
Yi = 0 + 1Xi + ui
where
Yi = after
i
Y – before
i
Y
Xi = 1 if treated, = 0 otherwise
 1
ˆ
 is the diffs-in-diffs estimator

11-18
The differences-in-differences estimator, ctd.
(2) Equivalent “panel data” version:
Yit = 0 + 1Xit + 2Dit + 3Git + vit, i = 1,…,n
where
t = 1 (before experiment), 2 (after experiment)
Dit = 0 for t = 1, = 1 for t = 2
Git = 0 for control group, = 1 for treatment group
Xit = 1 if treated, = 0 otherwise
= Dit Git = interaction effect of being in treatment
group in the second period
 1
ˆ
 is the diffs-in-diffs estimator

11-19
Including additional subject characteristics (W’s)
 Typically you observe additional subject characteristics,
W1i,…,Wri
 Differences estimator with add’l regressors:
Yi = 0 + 1Xi + 2W1i + … + r+1Wri + ui
 Differences-in-differences estimator with W’s:
Yi = 0 + 1Xi + 2W1i + … + r+1Wri + ui
where Yi = after
i
Y – before
i
Y .

11-20
Why include additional subject characteristics (W’s)?
1. Efficiency: more precise estimator of 1 (smaller
standard errors)
2. Check for randomization. If X is randomly assigned,
then the OLS estimators with and without the W’s
should be similar – if they aren’t, this suggests that X
wasn’t randomly designed (a problem with the expt.)
 Note: To check directly for randomization,
regress X on the W’s and do a F-test.
3. Adjust for conditional randomization (we’ll return to
this later…)

11-21
Estimation when there is partial compliance
Consider diffs-in-diffs estimator, X = actual treatment
Yi = 0 + 1Xi + ui
 Suppose there is partial compliance: some of the
treated don’t take the drug; some of the controls go to
job training anyway
 Then X is correlated with u, and OLS is biased
 Suppose initial assignment, Z, is random
 Then (1) corr(Z,X) 0 and (2) corr(Z,u) = 0
 Thus 1 can be estimated by TSLS, with instrumental
variable Z = initial assignment
 This can be extended to W’s (included exog. variables)

11-22
Experimental Estimates of the Effect of
Reduction: The Tennessee Class Size Experiment
(SW Section 11.4)
Project STAR (Student-Teacher Achievement Ratio)
 4-year study, $12 million
 Upon entering the school system, a student was
randomly assigned to one of three groups:
oregular class (22 – 25 students)
oregular class + aide
osmall class (13 – 17 students)
 regular class students re-randomized after first year to
regular or regular+aide
 Y = Stanford Achievement Test scores

11-23
Deviations from experimental design
 Partial compliance:
o10% of students switched treatment groups because
of “incompatibility” and “behavior problems” – how
much of this was because of parental pressure?
oNewcomers: incomplete receipt of treatment for
those who move into district after grade 1
 Attrition
ostudents move out of district
ostudents leave for private/religious schools

11-24
Regression analysis
 The “differences” regression model:
Yi = 0 + 1SmallClassi + 2RegAidei + ui
where
SmallClassi = 1 if in a small class
RegAidei = 1 if in regular class with aide
 Additional regressors (W’s)
oteacher experience
ofree lunch eligibility
ogender, race

11-25
Differences estimates (no W’s)

11-27
How big are these estimated effects?
 Put on same basis by dividing by std. dev. of Y
 Units are now standard deviations of test scores

11-28
How do these estimates compare to those from the
California, Mass. observational studies? (Ch. 4 – 7)

11-29
Summary: The Tennessee Class Size Experiment
Remaining threats to internal validity
 partial compliance/incomplete treatment
ocan use TSLS with Z = initial assignment
oTurns out, TSLS and OLS estimates are similar
(Krueger (1999)), so this bias seems not to be large
Main findings:
 The effects are small quantitatively (same size as
gender difference)
 Effect is sustained but not cumulative or increasing
biggest effect at the youngest grades

11-30
What is the Difference Between a Control Variable
and the Variable of Interest?
(SW App. 11.3)
Example: “free lunch eligible” in the STAR regressions
 Coefficient is large, negative, statistically significant
 Policy interpretation: Making students ineligible for a
free school lunch will improve their test scores.
 Is this really an estimate of a causal effect?
 Is the OLS estimator of its coefficient unbiased?
 Can it be that the coefficient on “free lunch eligible”
is biased but the coefficient on SmallClass is not?

11-32
Example: “free lunch eligible,” ctd.
 Coefficient on “free lunch eligible” is large, negative,
statistically significant
 Policy interpretation: Making students ineligible for a
free school lunch will improve their test scores.
 Why (precisely) can we interpret the coefficient on
SmallClass as an unbiased estimate of a causal effect,
but not the coefficient on “free lunch eligible”?
 This is not an isolated example!
oOther “control variables” we have used: gender,
race, district income, state fixed effects, time fixed
effects, city (or state) population,…
 What is a “control variable” anyway?

11-33
Simplest case: one X, one control variable W
Yi = 0 + 1 Xi + 2Wi + ui
For example,
 W = free lunch eligible (binary)
 X = small class/large class (binary)
 Suppose random assignment of X depends on W
ofor example, 60% of free-lunch eligibles get small
class, 40% of ineligibles get small class)
onote: this wasn’t the actual STAR randomization
procedure – this is a hypothetical example
 Further suppose W is correlated with u

11-34
Yi = 0 + 1 Xi + 2Wi + ui
Suppose:
 The control variable W is correlated with u
 Given W = 0 (ineligible), X is randomly assigned
 Given W = 1 (eligible), X is randomly assigned.
Then:
 Given the value of W, X is randomly assigned;
 That is, controlling for W, X is randomly assigned;
 Thus, controlling for W, X is uncorrelated with u
 Moreover, E(u|X,W) doesn’t depend on X
 That is, we have conditional mean independence:
E(u|X,W) = E(u|W)

11-35
Implications of conditional mean independence
Yi = 0 + 1 Xi + 2Wi + ui
Suppose E(u|W) is linear in W (not restrictive – could add
quadratics etc.): then,
E(u|X,W) = E(u|W) = 0 + 1Wi (*)
so
E(Yi|Xi,Wi) = E(0 + 1 Xi + 2Wi + ui|Xi,Wi)
= 0 + 1Xi + 2Wi + E(ui|Xi,Wi)
= 0 + 1Xi + 2Wi + 0 + 1Wi by (*)
= (0+0) + 1Xi + (1+2)Wi

11-36
Implications of conditional mean independence:
 The conditional mean of Y given X and W is
E(Yi|Xi,Wi) = (0+0) + 1Xi + (1+2)Wi
 The effect of a change in X under conditional mean
independence is the desired causal effect:
E(Yi|Xi = x+x,Wi) – E(Yi|Xi = x,Wi) = 1x
or
1 =
( | , ) ( | , )
i i i i i i
E Y X x x W E Y X x W
x
    

 If X is binary (treatment/control), this becomes:
1 =
( | 1, ) ( | 0, )
i i i i i i
E Y X W E Y X W
x
  

which is the desired treatment effect.

11-37
Implications of conditional mean independence, ctd.
Yi = 0 + 1 Xi + 2Wi + ui
Conditional mean independence says:
E(u|X,W) = E(u|W)
which, with linearity, implies:
E(Yi|Xi,Wi) = (0+0) + 1Xi + (1+2)Wi
Then:
 The OLS estimator 1
ˆ
 is unbiased.
 2
ˆ
 is not consistent and not meaningful
 The usual inference methods (standard errors,
hypothesis tests, etc.) apply to 1
ˆ
 .

11-38
So, what is a control variable?
A control variable W is a variable that results in X
satisfying the conditional mean independence condition:
E(u|X,W) = E(u|W)
 Upon including a control variable in the regression, X
ceases to be correlated with the error term.
 The control variable itself can be (in general will be)
correlated with the error term.
 The coefficient on X has a causal interpretation.
 The coefficient on W does not have a causal
interpretation.

11-39
Example: Effect of teacher experience on test scores
More on the design of Project STAR:
 Teachers didn’t change school because of the expt.
 Within their normal school, teachers were randomly
assigned to small/regular/reg+aide classrooms.
 What is the effect of X = years of teacher education?
The design implies conditional mean independence:
 W = school binary indicator
 Given W (school), X is randomly assigned
 That is, E(u|X,W) = E(u|W)
 W is plausibly correlated with u (nonzero school fixed
effects: some schools are better/richer/etc than others)

11-41
Example: teacher experience, ctd.
 Without school fixed effects (2), the estimated effect of
an additional year of experience is 1.47 (SE = .17)
 “Controlling for the school” (3), the estimated effect of
an additional year of experience is .74 (SE = .17)
 Direction of bias makes sense:
oless experienced teachers at worse schools
oyears of experience picks up this school effect
 OLS estimator of coefficient on years of experience is
biased up without school effects; with school effects,
OLS yields unbiased estimator of causal effect
 School effect coefficients don’t have a causal
interpretation (effect of student changing schools)

11-42
Quasi-Experiments
(SW Section 11.5)
A quasi-experiment or natural experiment has a source
of randomization that is “as if” randomly assigned, but
this variation was not part of a conscious randomized
treatment and control design.
Two cases:
(a) Treatment (X) is “as if” randomly assigned (OLS)
(b) A variable (Z) that influences treatment (X) is
“as if” randomly assigned (IV)

11-43
Two types of quasi-experiments
(a) Treatment (X) is “as if” randomly assigned (perhaps
conditional on some control variables W)
 Ex: Effect of marginal tax rates on labor supply
oX = marginal tax rate (rate changes in one state,
not another; state is “as if” randomly assigned)
 Effect on survival of cardiac catheterization
X = cardiac catheterization;
Z = differential distance to CC hospital

11-44
Econometric methods
(a) Treatment (X) is “as if” randomly assigned (OLS)
Diffs-in-diffs estimator using panel data methods:
Yit = 0 + 1Xit + 2Dit + 3Git + uit, i = 1,…,n
where
t = 1 (before experiment), 2 (after experiment)
Dit = 0 for t = 1, = 1 for t = 2
Git = 0 for control group, = 1 for treatment group
Xit = 1 if treated, = 0 otherwise
= Dit Git = interaction effect of being in treatment
group in the second period
 1
ˆ
 is the diffs-in-diffs estimator…

11-45
The panel data diffs-in-diffs estimator simplifies to
the “changes” diffs-in-diffs estimator when T = 2
Yit = 0 + 1Xit + 2Dit + 3Git + uit, i = 1,…,n (*)
For t = 1: Di1 = 0 and Xi1 = 0 (nobody treated), so
Yi1 = 0 + 3Gi1 + ui1
For t = 2: Di2 = 1 and Xi2 = 1 if treated, = 0 if not, so
Yi2 = 0 + 1Xi2 + 2 + 3Gi2 + ui2
so
Yi = Yi2–Yi1 = (0+1Xi2+2+3Gi2+ui2) – (0+3Gi1+ui1)
= 1Xi + 2 + (ui1 – ui2) (since Gi1 = Gi2)
or
Yi = 2 + 1Xi + vi, where vi = ui1 – ui2 (**)

11-46
Differences-in-differences with control variables
Yit = 0 + 1Xit + 2Dit + 3Git + 4W1it + … + 3+rWrit + uit,
Xit = 1 if the treatment is received, = 0 otherwise
= Git Dit (= 1 for treatment group in second period)
 If the treatment (X) is “as if” randomly assigned,
given W, then u is conditionally mean indep. of X:
E(u|X,D,G,W) = E(u|D,G,W)
 OLS is a consistent estimator of 1, the causal effect
of a change in X
 In general, the OLS estimators of the other
coefficients do not have a causal interpretation.

11-47
Yit = 0 + 1Xit + 2Dit + 3Git + 4W1it + … + 3+rWrit + uit,
Xit = 1 if the treatment is received, = 0 otherwise
= Git Dit (= 1 for treatment group in second period)
Zit = variable that influences treatment but is
uncorrelated with uit (given W’s)
TSLS:
 X = endogenous regressor
 D,G,W1,…,Wr = included exogenous variables
 Z = instrumental variable

11-48
Potential Threats to Quasi-Experiments
(SW Section 11.6)
The threats to the internal validity of a quasi-
experiment are the same as for a true experiment, with
one addition.
4. Failure to randomize (imperfect randomization)
Is the “as if” randomization really random, so that X
(or Z) is uncorrelated with u?
5. Failure to follow treatment protocol & attrition
6. Experimental effects (not applicable)
7. Instrument invalidity (relevance + exogeneity)
(Maybe healthier patients do live closer to CC hospitals
–they might have better access to care in general)

11-49
The threats to the external validity of a quasi-
experiment are the same as for an observational study.
5. Nonrepresentative sample
6. Nonrepresentative “treatment” (that is, program or
policy)
Example: Cardiac catheterization
 The CC study has better external validity than
controlled clinical trials because the CC study uses
observational data based on real-world
implementation of cardiac catheterization.
However that study used data from the early 90’s – do its
findings apply to CC usage today?

11-50
Experimental and Quasi-Experiments Estimates in
Heterogeneous Populations
(SW Section 11.7)
 We have discussed “the” treatment effect
 But the treatment effect could vary across individuals:
oEffect of job training program probably depends on
education, years of education, etc.
oEffect of a cholesterol-lowering drug could depend
other health factors (smoking, age, diabetes,…)
 If this variation depends on observed variables, then
this is a job for interaction variables!
 But what if the source of variation is unobserved?

11-51
Heterogeneity of causal effects
When the causal effect (treatment effect) varies among
individuals, the population is said to be heterogeneous.
When there are heterogeneous causal effects that are not
linked to an observed variable:
 What do we want to estimate?
oOften, the average causal effect in the population
oBut there are other choices, for example the average
causal effect for those who participate (effect of
treatment on the treated)
 What do we actually estimate?
ousing OLS? using TSLS?

11-52
Population regression model with heterogeneous
causal effects:
Yi = 0 + 1iXi + ui, i = 1,…,n
 1i is the causal effect (treatment effect) for the ith
individual in the sample
 For example, in the JTPA experiment, 1i could be zero
if person i already has good job search skills
 What do we want to estimate?
oeffect of the program on a randomly selected person
(the “average causal effect”) – our main focus
oeffect on those most (least?) benefited
oeffect on those who choose to go into the program?

11-53
The Average Causal Effect
Yi = 0 + 1iXi + ui, i = 1,…,n
 The average causal effect (or average treatment effect)
is the mean value of 1i in the population.
 We can think of 1 as a random variable: it has a
distribution in the population, and drawing a different
person yields a different value of 1 (just like X and Y)
 For example, for person #34 the treatment effect is not
random – it is her true treatment effect – but before she
is selected at random from the population, her value of
1 can be thought of as randomly distributed.

11-54
The average causal effect, ctd.
Yi = 0 + 1iXi + ui, i = 1,…,n
 The average causal effect is E(1).
 What does OLS estimate:
(a) When the conditional mean of u given X is zero?
(b)Under the stronger assumption that X is randomly
assigned (as in a randomized experiment)?
In this case, OLS is a consistent estimator of the
average causal effect.

11-55
OLS with Heterogeneous Causal Effects
Yi = 0 + 1iXi + ui, i = 1,…,n
(a) Suppose E(ui|Xi) = 0 so cov(ui,Xi) = 0.
 If X is binary (treated/untreated), 1
ˆ
 = treated
Y – control
Y
estimates the causal effect among those who receive
the treatment.
 Why? For those treated, treated
Y reflects the effect of
the treatment on them. But we don’t know how the
untreated would have responded had they been
treated!

11-57
OLS with heterogeneous treatment effects: general X
with E(ui|Xi) = 0
1
ˆ
 = 2
XY
X
s
s
p
 2
XY
X


= 0 1
cov( , )
var( )
i i i i
i
X u X
X
 
 
= 0 1
cov( , ) cov( , ) cov( , )
var( )
i i i i i i
i
X X X u X
X
 
 
= 1
cov( , )
var( )
i i i
i
X X
X

(because cov(ui,Xi) = 0)
 If X is binary, this simplifies to the “effect of
treatment on the treated”
 Without heterogeneity, 1i = 1 and 1
ˆ

p
 1
 In general, the treatment effects of individuals with
large values of X are given the most weight

11-58
(b) Now make a stronger assumption: that X is randomly
assigned (experiment or quasi-experiment). Then
what does OLS actually estimate?
 I Xi is randomly assigned, it is distributed
independently of 1i, so there is no difference
between the population of controls and the
population in the treatment group
 Thus the effect of treatment on the treated = the
average treatment effect in the population.

11-59
The math:
1
ˆ

p

1
cov( , )
var( )
i i i
i
X X
X

= 1
1
cov( , )
|
var( )
i i i
i
i
X X
E E
X


 
 
 
 
 
 
= 1
cov( , )
var( )
i i
i
i
X X
E
X

 
 
 
= 1
var( )
var( )
i
i
i
X
E
X

 
 
 
= E(1i)
Summary
 If Xi and 1i are independent (Xi is randomly
assigned), OLS estimates the average treatment effect.
 If Xi is not randomly assigned but E(ui|Xi) = 0, OLS
estimates the effect of treatment on the treated.
 Without heterogeneity, the effect of treatment on the
treated and the average treatment effect are the same

11-60
IV Regression with Heterogeneous Causal Effects
Suppose the treatment effect is heterogeneous and the
effect of the instrument on X is heterogeneous:
Yi = 0 + 1iXi + ui (equation of interest)
Xi = 0 + 1iZi + vi (first stage of TSLS)
In general, TSLS estimates the causal effect for those
whose value of X (probability of treatment) is most
influenced by the instrument.

11-61
IV with heterogeneous causal effects, ctd.
Intuition:
 Suppose 1i’s were known. If for some people 1i =
0, then their predicted value of Xi wouldn’t depend
on Z, so the IV estimator would ignore them.
 The IV estimator puts most of the weight on
individuals for whom Z has a large influence on X.
 TSLS measures the treatment effect for those whose
probability of treatment is most influenced by X.

11-62
The math…
To simplify things, suppose:
 1i and 1i are distributed independently of (ui,vi,Zi)
 E(ui|Zi) = 0 and E(vi|Zi) = 0
 E(1i) 0
Then 1
ˆTSLS

p
 1 1
1
( )
( )
i i
i
E
E
 

(derived in SW App. 11.4)
 TSLS estimates the causal effect for those individuals
for whom Z is most influential (those with large 1i).

11-63
When there are heterogeneous causal effects, what
TSLS estimates depends on the choice of instruments!
 With different instruments, TSLS estimates different
weighted averages!!!
 Suppose you have two instruments, Z1 and Z2.
oIn general these instruments will be influential for
different members of the population.
oUsing Z1, TSLS will estimate the treatment effect for
those people whose probability of treatment (X) is
most influenced by Z1
oThe treatment effect for those most influenced by Z1
might differ from the treatment effect for those most
influenced by Z2

11-64
When does TSLS estimate the average causal effect?
1
ˆTSLS

p
 1 1
1
( )
( )
i i
i
E
E
 

 TSLS estimates the average causal effect (that is,
1
ˆTSLS

p
 E(1i)) if:
oIf 1i and 1i are independent
oIf 1i = 1 (no heterogeneity in equation of interest)
oIf 1i = 1 (no heterogeneity in first stage equation)
 But in general 1
ˆTSLS
 does not estimate E(1i)!

11-65
Example: Cardiac catheterization
Yi = survival time (days) for AMI patients
Xi = received cardiac catheterization (or not)
Zi = differential distance to CC hospital
Equation of interest:
SurvivalDaysi = 0 + 1iCardCathi + ui
First stage (linear probability model):
CardCathi = 0 + 1iDistancei + vi
 For whom does distance have the great effect on the
probability of treatment?
 For those patients, what is their causal effect 1i?

11-66
Equation of interest:
SurvivalDaysi = 0 + 1iCardCathi + ui
First stage (linear probability model):
CardCathi = 0 + 1iDistancei + vi
 TSLS estimates the causal effect for those whose
value of Xi is most heavily influenced by Zi
 TSLS estimates the causal effect for those for whom
distance most influences the probability of treatment
 What is their causal effect? (“We might as well go to
the CC hospital, its not too much farther”)
 This is one explanation of why the TSLS estimate is
smaller than the clinical trial OLS estimate.

11-67
Heterogeneous Causal Effects: Summary
 Heterogeneous causal effects means that the causal (or
treatment) effect varies across individuals.
 When these differences depend on observable variables,
heterogeneous causal effects can be estimated using
interactions (nothing new here).
 When these differences are unobserved (1i) the
average causal (or treatment) effect is the average value
in the population, E(1i).
 When causal effects are heterogeneous, OLS and TSLS
estimate….

11-68
OLS with Heterogeneous Causal Effects
X is: Relation between Xi and
ui:
Then OLS estimates:
binary E(ui|Xi) = 0 effect of treatment on the
treated: E(1i|Xi=1)
X randomly assigned (so
Xi and ui are independent)
average causal effect E(1i)
general E(ui|Xi) = 0 weighted average of 1i,
placing most weight on
those with large |Xi–X|
X randomly assigned average causal effect E(1i)
Without heterogeneity, 1i = 1 and 1
ˆ

p
 1 in all these
cases.

11-69
TSLS with Heterogeneous Causal Effects
 TSLS estimates the causal effect for those individuals
for whom Z is most influential (those with large 1i).
 What TSLS estimates depends on the choice of Z!!
 In CC example, these were the individuals for whom
the decision to drive to a CC lab was heavily
influenced by the extra distance (those patients for
whom the EMT was otherwise “on the fence”)
 Thus TSLS also estimates a causal effect: the average
effect of treatment on those most influenced by the
instrument
oIn general, this is neither the average causal effect
nor the effect of treatment on the treated

11-70
Summary: Experiments and Quasi-Experiments
(SW Section 11.8)
Experiments:
 Average causal effects are defined as expected values
of ideal randomized controlled experiments
 Actual experiments have threats to internal validity
 These threats to internal validity can be addressed (in
part) by:
opanel methods (differences-in-differences)
omultiple regression
oIV (using initial assignment as an instrument)

11-71
Summary, ctd.
Quasi-experiments:
 Quasi-experiments have an “as-if” randomly assigned
source of variation.
 This as-if random variation can generate:
oXi which satisfies E(ui|Xi) = 0 (so estimation
proceeds using OLS); or
oinstrumental variable(s) which satisfy E(ui|Zi) = 0
(so estimation proceeds using TSLS)
 Quasi-experiments also have threats to internal vaidity

11-72
Summary, ctd.
Two additional subtle issues:
 What is a control variable?
oA variable W for which X and u are uncorrelated,
given the value of W (conditional mean
independence: E(ui|Xi,Wi) = E(ui|Wi)
oExample: STAR & effect of teacher experience
 within their school, teachers were randomly
assigned to regular/reg+aide/small class
 OLS provides an unbiased estimator of the causal
effect, but only after controlling for school
effects.

11-73
Summary, ctd.
 What do OLS and TSLS estimate when there is
unobserved heterogeneity of causal effects?
 In general, weighted averages of causal effects:
oIf X is randomly assigned, then OLS estimates the
average causal effect.
oIf Xi is not randomly assigned but E(ui|Xi) = 0, OLS
estimates the average effect of treatment on the
treated.
o If E(ui|Zi) = 0, TSLS estimates the average effect of
treatment on those most influenced by Zi.

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