Economics 696, Causal Inference and Program Evaluation
Problem Set 1: due Friday, Feb 10 (in my mail box)
1. Consider the data in Table 1. Assume they arose from a randomized experiment
in which an equal number of units were selected to be given the treat
Economics 696, Causal Inference and Program Evaluation
Lecture Note 10: Imbens and Newey (2004)
Consider the following model:
T = h ( Z , ),
(1)
Y = g (T , ).
(2)
We interpret Y as the outcome, and T as the (observed) treatment.
The functions h and g are
Economics 696: Lecture Note 11
Partial Identication and Bounds
This is based on the Manski articles; the book Identication Problems in the Social Sciences is
also an excellent source for material on bounds.
Censored Data
Suppose that Y and X are variables
Economics 696, Causal Inference and Program Evaluation
Lecture Note 12: Estimation and Inference for Bounds
1. Estimation of the identied set
Lets continue with the problem of estimating the distribution of a variable subject to missing
data. As before, Y
Economics 696: Lecture Note 13
Differences-in-Differences
Example: Card (1990) Study of the Mariel Boatlift
Q: Do low-skilled immigrants displace low-skilled US citizens in the labor market?
Intervention: Mariel Boatlift, a large-scale migration of Cubans
Economics 696: Lecture Note 14
Regression Discontinuity Design
Examples:
Thistlewaite and Campbell (1960): scholarship and career choice
van der Klaauw (1997): nancial aid and enrollment in college
Angrist and Lavy (1997): class size and test scores
B
Economics 696: Lecture Note 15
Treatment Assignment as a Statistical Decision Problem
Note: this is based on Manski (2004); however, I have changed the notation considerably.
Basic Setup
We imagine a social planner who must assign an individual to one of
Economics 696, Causal Inference
Minipaper Project Instructions
Your minipaper should be a short, focused paper that applies the concepts and methods from
the course to a specic empirical problem.
You may choose the specic topic, and you are encouraged to
Economics 696, Causal Inference and Program Evaluation
Lecture Note 9: Instrumental Variables and Control Functions
Based on Heckman (1979), Heckman and Robb (1985), and Newey, Powell, and Vella (1999).
Consider a conventional linear IV model, where the e
Economics 696, Causal Inference and Program Evaluation
Lecture Note 8: Instrumental Variables
1 Wald Estimator
Suppose we are interested in estimating the effect of a treatment T on an outcome Y ,
and we believe the treatment does not satisfy the random a
Economics 696, Causal Inference and Program Evaluation
Lecture Note 7: Multivalued Treatments, Unconfoundedness, and the Generalized Propensity
Score
Multivalued Treatments
Next, suppose that the treatment can take on more than two values:
T T = cfw_t 1 ,
Economics 696F, Causal Inference and Program Evaluation
Problem Set 2: due Friday, March 2 (revised 2/25/11)
1. Revisit the data in ps1b.txt and the setup in HW1, Question 3.
(a) Assume the propensity score has the form
P r (T = 1 | X = x ) =
exp(1 + x 2
Economics 696F, Causal Inference and Program Evaluation
Problem Set 3: due Monday, April 9
Note: you should be working on your mini paper as well.
1. Consider a binary treatment setup, with a binary outcome and binary covariate X . Assume
unconfoundedness
Economics 696F, Causal Inference and Program Evaluation
Lecture Note 1: Econometrics without Error Terms
Read articles: Holland (with discussion), Rubin 1974, Lalonde, Rosenbaum ch.2
T = some treatment
For simplicity, assume binary, so T = 0, 1. (We will
Economics 696F, Causal Inference and Program Evaluation
Lecture Note 2: Potential Outcomes and Randomized Experiments
1 Potential Outcomes Model
Recall: for units i = 1, . . . , n ,
Ti
= 0, 1 treatment
Yi (0) =
potential outcome under control
Yi (1) =
pot
Economics 696, Causal Inference and Program Evaluation
Lecture Note 3: Unconfounded Treatment Assignment and Regression Analysis
1 Causal Model and Unconfoundedness
Next we turn to observational studies, where treatment assignment is not under our control
Economics 696, Causal Inference and Program Evaluation
Lecture Note 4: The Propensity Score
We continue to make the unconfoundedness assumption, and focus on estimating the ATE and
the TT.
Note: in order to simplify the notation a bit, I will sometimes dr
Economics 696, Causal Inference and Program Evaluation
Lecture Note 5: Asymptotics for Weighting Estimators and Efciency of Estimated Weights
Asymptotics for Weighting Estimator
Consider the estimator
=
1 n Ti Yi
(1 Ti )Yi
.
n i =1 p ( X i ) 1 p ( X i )
F
Economics 696, Causal Inference and Program Evaluation
Lecture Note 6: Bootstrap
These notes are based on Politis, Romano, and Wolf (1999), Ch.1. See also Efron (1979) where the
bootstrap was rst introduced.
Sometimes, it is difcult to calculate variance
ASSESSMENT BRIEF
Subject Code and Title
ECON6000: Economic Principles and Decision Making
Assessment
Assessment 3: Report
Individual/Group
Individual
Length
NA
Learning Outcomes
1. Interpret and successfully apply economic concepts of
supply and demand fo