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B019297 - CAUSAL INFERENCE AND EVALUATION METHODS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2015-16
Course year
Second year - First Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
SECS-S/01 - STATISTICS
Credits
9
Teaching Hours
72
Teaching Term
14/09/2015 ⇒ 04/12/2015
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Teaching Language
Italian/English
Course Content
Statistical
methods
are
discussed
for
inferring
causal
effects
from
data
from
randomized
experiments
or
observational
studies.
Examples
will
come
from
many
disciplines:
economics,
education,
other
social
sciences,
epidemiology,
and
biomedical
science.
Specific
examples
include
evaluations
of
job
training
programs,
educational
voucher
schemes,
medical
treatments,
smoking,
and
military
service.
methods
are
discussed
for
inferring
causal
effects
from
data
from
randomized
experiments
or
observational
studies.
Examples
will
come
from
many
disciplines:
economics,
education,
other
social
sciences,
epidemiology,
and
biomedical
science.
Specific
examples
include
evaluations
of
job
training
programs,
educational
voucher
schemes,
medical
treatments,
smoking,
and
military
service.
Suggested readings (Search our library's catalogue)
The
primary
textbook
is
"Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction",
by
Guido
W.
Imbens
and
Donald
B.
Rubin,
which
is
in
the
final
draft
stage and will be published at the end of 2014 by Cambridge University Press.
Chapters
from
the
textbook
will
bedistributed to students if book not available.
Additional
journal
articles
for
discussion
will
also
be
made
available.
primary
textbook
is
"Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction",
by
Guido
W.
Imbens
and
Donald
B.
Rubin,
which
is
in
the
final
draft
stage and will be published at the end of 2014 by Cambridge University Press.
Chapters
from
the
textbook
will
bedistributed to students if book not available.
Additional
journal
articles
for
discussion
will
also
be
made
available.
Learning Objectives
Students
will
develop
expertise
to
assess
the
credibility
of
causal
claims
and
the
ability
to
apply
the
relevant
statistical
methods
for
causal
analyses.
will
develop
expertise
to
assess
the
credibility
of
causal
claims
and
the
ability
to
apply
the
relevant
statistical
methods
for
causal
analyses.
Prerequisites
Statistical inference
Teaching Methods
Lectures, presentations of case studies.
Type of Assessment
Final score will be based on evaluation of home assignment, 1 mid-term exam, one final project with oral discussion.
Course program
Part
I:
The
Basic
Framework
-
A
Brief
History
of
the
Potential
Outcome
Approach
to
Causal
Inference
-
A
Taxonomy
of
Assignment
Mechanisms
Part
II:
Classical
Randomized
Experiments
-
A
Taxonomy
of
Classical
Randomized
Experiments
-
Fisher’s
Exact
P‐values
for
Completely
Randomized
Experiments
-
Neyman’s
Repeated
Sampling
Approach
to
Completely
Randomized
Experiments
‐
Regression
Methods
for
Completely
Randomized
Experiments
-
Model‐based
Inference
in
Completely
Randomized
Experiments
-
Stratified
Randomized
Experiments
-
Paired
Randomized
Experiments
Part
III
Regular
Assignment
Mechanisms
–
Unconfounded
Treatment
Assignment
–
Estimating
the
Propensity
Score
–
Assessing
Overlap
in
Covariate
Distributions
-
Design
in
Observational
Studies:
Matching
and
Subclassification - Sensitivity Anlalysis
Part
IV:
Irregular
Assignment
Mechanisms
Non‐Compliance
and
Instrumental
Variables
Analysis -
Principal
Stratification
- Additional
topics
depending
on
interest
and
potentially
including: Rgression Discontinuity Designs, Broken Randomized Experiments, Recent
Developments in Causal Inference
I:
The
Basic
Framework
-
A
Brief
History
of
the
Potential
Outcome
Approach
to
Causal
Inference
-
A
Taxonomy
of
Assignment
Mechanisms
Part
II:
Classical
Randomized
Experiments
-
A
Taxonomy
of
Classical
Randomized
Experiments
-
Fisher’s
Exact
P‐values
for
Completely
Randomized
Experiments
-
Neyman’s
Repeated
Sampling
Approach
to
Completely
Randomized
Experiments
‐
Regression
Methods
for
Completely
Randomized
Experiments
-
Model‐based
Inference
in
Completely
Randomized
Experiments
-
Stratified
Randomized
Experiments
-
Paired
Randomized
Experiments
Part
III
Regular
Assignment
Mechanisms
–
Unconfounded
Treatment
Assignment
–
Estimating
the
Propensity
Score
–
Assessing
Overlap
in
Covariate
Distributions
-
Design
in
Observational
Studies:
Matching
and
Subclassification - Sensitivity Anlalysis
Part
IV:
Irregular
Assignment
Mechanisms
Non‐Compliance
and
Instrumental
Variables
Analysis -
Principal
Stratification
- Additional
topics
depending
on
interest
and
potentially
including: Rgression Discontinuity Designs, Broken Randomized Experiments, Recent
Developments in Causal Inference