"Semiparametric Estimation of Instrumental Variable Models 
for Causal Effects"

Alberto Abadie

ABSTRACT

	This article introduces a new class of instrumental variable
(IV) estimators of causal treatment effects for linear and nonlinear
models with covariates. The rationale for focusing on nonlinear models
is to improve the approximation to the causal response function of
interest. For example, if the dependent variable is binary or limited,
or if the effect of the treatment is affected by covariates, a
nonlinear model is likely to be appropriate. However, identification
is not attained through functional form restrictions. This paper shows
how to estimate a well-defined approximation to a nonlinear causal
response function of unknown functional form using simple parametric
models. As an important special case, I introduce a linear model that
provides the best linear approximation to an underlying causal
relation. It is shown that Two Stage Least Squares (2SLS) does not
always have this property and some possible interpretations of 2SLS
coefficients are briefly studied. The ideas and estimators in this
paper are illustrated using instrumental variables to estimate the
effects of 401(k) retirement programs on savings.