Imbens/Wooldridge, Lecture Notes 1, Summer ’07
1
What’s New in Econometrics
NBER, Summer 2007
Lecture 1, Monday, July 30th, 9.0010.30am
Estimation of Average Treatment Effects Under Unconfoundedness
1. Introduction
In this lecture we look at several methods for estimating average effects of a program,
treatment, or regime, under unconfoundedness. The setting is one with a binary program.
The traditional example in economics is that of a labor market program where some individ
uals receive training and others do not, and interest is in some measure of the effectiveness
of the training. Unconfoundedness, a term coined by Rubin (1990), refers to the case where
(nonparametrically) adjusting for differences in a fixed set of covariates removes biases in
comparisons between treated and control units, thus allowing for a causal interpretation of
those adjusted differences. This is perhaps the most important special case for estimating
average treatment effects in practice. Alternatives typically involves strong assumptions link
ing unobservables to observables in specific ways in order to allow adjusting for the relevant
differences in unobserved variables. An example of such a strategy is instrumental variables,
which will be discussed in Lecture 3.
A second example that does not involve additional
assumptions is the bounds approach developed by Manski (1990, 2003).
Under the specific assumptions we make in this setting, the population average treat
ment effect can be estimated at the standard parametric
√
N
rate without functional form
assumptions. A variety of estimators, at first sight quite different, have been proposed for
implementing this. The estimators include regression estimators, propensity score based es
timators and matching estimators. Many of these are used in practice, although rarely is
this choice motivated by principled arguments. In practice the differences between the esti
mators are relatively minor when applied appropriately, although matching in combination
with regression is generally more robust and is probably the recommended choice. More im
portant than the choice of estimator are two other issues. Both involve analyses of the data
without the outcome variable.
First, one should carefully check the extent of the overlap
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Imbens/Wooldridge, Lecture Notes 1, Summer ’07
2
in covariate distributions between the treatment and control groups. Often there is a need
for some trimming based on the covariate values if the original sample is not well balanced.
Without this, estimates of average treatment effects can be very sensitive to the choice of,
and small changes in the implementation of, the estimators.
In this part of the analysis
the propensity score plays an important role. Second, it is useful to do some assessment of
the appropriateness of the unconfoundedness assumption. Although this assumption is not
directly testable, its plausibility can often be assessed using lagged values of the outcome as
pseudo outcomes. Another issue is variance estimation. For matching estimators bootstrap
ping, although widely used, has been shown to be invalid. We discuss general methods for
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 Fall '08
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 Econometrics, Least Squares, Linear Regression, Regression Analysis, WI, Propensity score, W Yi

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