- The document discusses methods for estimating the impact of programs, specifically average treatment effects.
- It introduces the concept of potential outcomes (Yi1 if an individual participates in the program, Yi0 if they do not) and defines the average treatment effect as the expected difference between these potential outcomes (E[Yi1 - Yi0]).
- Estimating the average treatment effect is challenging because for each individual we only observe one potential outcome (either Yi1 or Yi0 but not both). The document explores how to separately estimate the expected outcomes under treatment and non-treatment to calculate the average treatment effect.
1. Peter M. Lance, PhD
MEASURE Evaluation
University of North Carolina at
Chapel Hill
MARCH 31, 2016
Fundamentals of Program
Impact Evaluation
2. Global, five-year, $180M cooperative agreement
Strategic objective:
To strengthen health information systems – the
capacity to gather, interpret, and use data – so
countries can make better decisions and sustain good
health outcomes over time.
Project overview
3. Improved country capacity to manage health
information systems, resources, and staff
Strengthened collection, analysis, and use of
routine health data
Methods, tools, and approaches improved and
applied to address health information challenges
and gaps
Increased capacity for rigorous evaluation
Phase IV Results Framework
18. Did the program cause a change in an
outcome of interest Y ?
(Causality)
19. Our outcome of Interest
What happens if an individual does
not participate in a program
What happens if that individual does
participate in a program
Potential Outcomes
𝑌 :
𝑌0
:
𝑌1
:
20. Our outcome of interest
What happens if an individual does
not participate in a program
What happens if that individual does
participate in a program
Potential Outcomes
𝑌𝑖 :
𝑌𝑖
0
:
𝑌𝑖
1
:
21. Our outcome of interest
What happens if an individual does
not participate in a program
What happens if that individual does
participate in a program
Potential Outcomes
𝑌𝑖 :
𝑌𝑖
0
:
𝑌𝑖
1
:
22. Our outcome of interest
What happens if an individual does
not participate in a program
What happens if that individual does
participate in a program
Potential Outcomes
𝑌𝑖 :
𝑌𝑖
0
:
𝑌𝑖
1
:
23. What happens
if the individual
participates
{Causal} Program Impact
𝑌𝑖
1
− 𝑌𝑖
0
= Program Impact
What happens
if the individual
does not
participate
24. What happens
if the individual
participates
{Causal} Program Impact
𝑌𝑖
1
− 𝑌𝑖
0
= Program Impact
What happens
if the individual
does not
participate
25. What happens
if the individual
participates
{Causal} Program Impact
𝑌𝑖
1
− 𝑌𝑖
0
= Program Impact
What happens
if the individual
does not
participate
26. What happens
if the individual
participates
{Causal} Program Impact
𝑌𝑖
1
− 𝑌𝑖
0
= Program Impact
What happens
if the individual
does not
participate
27. What happens
if the individual
participates
{Causal} Program Impact
𝑌𝑖
1
− 𝑌𝑖
0
= Program Impact
What happens
if the individual
does not
participate
28. 𝑃𝑖 =
1ifindividual 𝑖 participates
0if individual 𝑖 does not participate
Program Participation
50. An expected value for a random variable is the
average value from a large number of repetitions
of the experiment that random variable represents
An expected value is the true average of a random
variable across a population
Expected Value
51. An expected value for a random variable is the
average value from a large number of repetitions
of the experiment that random variable represents
An expected value is the true average of a random
variable across a population
Expected Value
52. An expected value is the true average of a random
variable across a population
𝐸 𝑋 = sometruevalue
Expected Value
66. Average Treatment Effect (ATE)
𝐸 𝑌1 − 𝑌0
Average Effect of Treatment on the Treated (ATT)
𝐸 𝑌1 − 𝑌0|𝑃 = 1
Treatment Effects
67.
68.
69. Suppose that we have a sample of 𝑖 = 1,…, 𝑛
individuals….
…but for each individual 𝑖 we observe either
𝑌𝑖
1
or 𝑌𝑖
0
…
…but not both
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
70. Suppose that we have a sample of 𝑖 = 1,…, 𝑛
individuals….
…but for each individual 𝑖 we observe either
𝑌𝑖
1
or 𝑌𝑖
0
…
…but not both
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
71. Remember, however, a key property of expectations:
𝐸 𝑌1
− 𝑌0
= 𝐸 𝑌1
− 𝐸 𝑌0
…but this means that in principle we could
estimate E 𝑌1
and E 𝑌0
separately
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
72. Remember, however, a key property of expectations:
𝐸 𝑌1
− 𝑌0
= 𝐸 𝑌1
− 𝐸 𝑌0
…but this means that in principle we could
estimate E 𝑌1
and E 𝑌0
separately
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
73. For instance, suppose that in our sample we have:
𝑛 𝑃
participants(𝑃𝑖 = 1)
and
𝑛 𝑁
non-participants(𝑃𝑖 = 0)
(hence 𝑛 𝑃
+ 𝑛 𝑁
= 𝑛)
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
74. Then an estimator of 𝐸 𝑌1
is
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
calculated with the 𝑛 𝑃
participants out of the
sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
75. Then an estimator of 𝐸 𝑌1
is
𝒀 𝟏 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
calculated with the 𝑛 𝑃
participants out of the
sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
76. Then an estimator of 𝐸 𝑌1
is
𝑌1 =
𝑗=1
𝒏 𝑷
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
calculated with the 𝑛 𝑃
participants out of the
sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
77. Then an estimator of 𝐸 𝑌1
is
𝑌1 =
𝑗=1
𝑛 𝑃
𝒀𝒋
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
calculated with the 𝑛 𝑃
participants out of the
sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
78. Then an estimator of 𝐸 𝑌1
is
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝒏 𝑷
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
calculated with the 𝑛 𝑃
participants out of the
sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
79. Then an estimator of 𝐸 𝑌1
is
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝒀𝒋
𝟏
𝑛 𝑃
calculated with the 𝑛 𝑃
participants out of the
sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
80. Similarly, an estimator of 𝐸 𝑌0
is
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
calculated with the 𝑛 𝑁
non-participants out of
the sample of 𝑛 individuals
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
81. So then an estimate of
𝐸 𝑌1
− 𝑌0
= 𝐸 𝑌1
− 𝐸 𝑌0
is
𝑌1 − 𝑌0 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
−
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
−
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
So how do we estimate
𝑬 𝒀 𝟏
− 𝒀 𝟎
??
86. So we have two samples of size 𝒏
By random chance, between the two samples we almost surely have
1. A different precise mix of individuals
2. A different number of participants (𝑛 𝑃) and non-participants (𝑛 𝑁)
3. Different estimates 𝑌1 and 𝑌0 of 𝐸 𝑌1 and 𝐸 𝑌0 :
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
87. So we have two samples of size 𝒏
By random chance, between the two samples we almost surely have
1. A different precise mix of individuals
2. A different number of participants (𝑛 𝑃) and non-participants (𝑛 𝑁)
3. Different estimates 𝑌1 and 𝑌0 of 𝐸 𝑌1 and 𝐸 𝑌0 :
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
88. So we have two samples of size 𝒏
By random chance, between the two samples we almost surely have
1. A different precise mix of individuals
2. A different number of participants (𝑛 𝑃) and non-participants (𝑛 𝑁)
3. Different estimates 𝑌1 and 𝑌0 of 𝐸 𝑌1 and 𝐸 𝑌0 :
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
89. So we have two samples of size 𝒏
By random chance, between the two samples we almost surely have
1. A different precise mix of individuals
2. A different number of participants (𝑛 𝑃) and non-participants (𝑛 𝑁)
3. Different estimates 𝑌1 and 𝑌0 of 𝐸 𝑌1 and 𝐸 𝑌0 :
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
90. So we have two samples of size 𝒏
By random chance, between the two samples we almost surely have
1. A different precise mix of individuals
2. A different number of participants (𝑛 𝑃) and non-participants (𝑛 𝑁)
3. Different estimates 𝑌1 and 𝑌0 of 𝐸 𝑌1 and 𝐸 𝑌0 :
𝑌1 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
=
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
91. So we have two samples of size 𝒏
By random chance, between the two samples we almost surely have
1. A different precise mix of individuals
2. A different number of participants (𝑛 𝑃) and non-participants (𝑛 𝑁)
3. Different estimates 𝑌1 and 𝑌0 of 𝐸 𝑌1 and 𝐸 𝑌0 :
𝒀 𝟏 =
𝒋=𝟏
𝒏 𝑷
𝒀𝒋
𝒏 𝑷
=
𝒋=𝟏
𝒏 𝑷
𝒀𝒋
𝟏
𝒏 𝑷
𝑌0 =
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
𝒀 𝟏 𝑬 𝒀 𝟏
146. The estimator
𝑌1 − 𝑌0 =
𝑗=1
𝑛 𝑃
𝑌𝑗
𝑛 𝑃
−
𝑘=1
𝑛 𝑁
𝑌𝑘
𝑛 𝑁
=
𝑘=1
𝑛 𝑁
𝑌𝑘
0
𝑛 𝑁
−
𝑗=1
𝑛 𝑃
𝑌𝑗
1
𝑛 𝑃
of
𝐸 𝑌1
− 𝑌0
would be biased if some individuals occurred only
among participants or non-participants
Or
more often among one of the two groups
150. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
151. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
152. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
153. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
154. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
155. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
156. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
157. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
158. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
159. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
160. Strength: How strong is the relationship?
Consistency: How consistently is link found?
Specificity: How specific is the setting or disease?
Temporality: Does the cause precede the effect?
Gradient: Does more cause lead to more effect?
Analogy: Do similar “causes” have similar effect?
Coherence: Are field and laboratory findings similar?
Experiment: Was variation in the cause random?
Plausibility: Does theory agree?
Bradford Hill Criteria
161. We are presented with data
in the form of a sample:
Causality: Our Approach
𝒀𝒊, 𝑷𝒊, 𝑿𝒊 ,
𝒊 = 𝟏, . . , 𝒏
162. We are presented with data
in the form of a sample:
Causality: Our Approach
𝒀𝒊, 𝑷𝒊, 𝑿𝒊 ,
𝒊 = 𝟏, . . , 𝒏
Assumptions Model
E(Y1-Y0),
E(Y1-Y0|P=1),
Etc.
163. We are presented with data
in the form of a sample:
Causality: Our Approach
𝒀𝒊, 𝑷𝒊, 𝑿𝒊 ,
𝒊 = 𝟏, . . , 𝒏
Assumptions Model
E(Y1-Y0),
E(Y1-Y0|P=1),
Etc.
166. MEASURE Evaluation is funded by the U.S. Agency
for International Development (USAID) under terms
of Cooperative Agreement AID-OAA-L-14-00004 and
implemented by the Carolina Population Center, University
of North Carolina at Chapel Hill in partnership with ICF
International, John Snow, Inc., Management Sciences for
Health, Palladium Group, and Tulane University. The views
expressed in this presentation do not necessarily reflect
the views of USAID or the United States government.
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