Two-stage-least-squares (2SLS) estimates are biased towards OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for...

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument...

In evaluation research, an average causal effect is usually defined as the expected difference between the outcomes of the treated, and what these outcomes would have been in the absence of treatment. This definition of causal effects makes sense for binary treatments only. In this paper, we...

Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous...

The average effect of social programs on outcomes such as earnings is a parameter of primary interest in econometric evaluations studies. New results on using exclusion restrictions to identify and estimate average treatment effects are presented. Identification is achieved given a minimum of...

This paper introduces an instrumental variables estimator for the effect of a binary treatment on the quantiles of potential outcomes. The quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regression as a special case when treatment status is...

We investigate the problem of predicting the average effect of a new training program using experiences with previous implementations. There are two principal complications in doing so. First, the population in which the new program will be implemented may differ from the population in which the...

Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as...

Matching estimators are widely used for the evaluation of programs or treatments. Often researchers use bootstrapping methods for inference. However, no formal justification for the use of the bootstrap has been provided. Here we show that the bootstrap is in general not valid, even in the...