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Identification of Causal Effects Using Instrumental Variables
It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
Identification and Estimation of Local Average Treatment Effects
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…
Large Sample Properties of Matching Estimators for Average Treatment Effects
Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of…
The Role of the Propensity Score in Estimating Dose-Response Functions
- G. Imbens
- 1 April 1999
Estimation of average treatment effects in observational, or non-experimental in pre-treatment variables. If the number of pre-treatment variables is large, and their distribution varies…
Implementing Matching Estimators for Average Treatment Effects in Stata
This paper presents an implementation of matching estimators for average treatment effects in Stata. The nnmatch command allows you to estimate the average effect for all units or only for the…
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions.
The Propensity Score with Continuous Treatments
of the binary treatment propensity score, which we label the generalized propensity score (GPS). We demonstrate that the GPS has many of the attractive properties of the binary treatment propensity…
Bias-Corrected Matching Estimators for Average Treatment Effects
In Abadie and Imbens (2006), it was shown that simple nearest-neighbor matching estimators include a conditional bias term that converges to zero at a rate that may be slower than N1/2. As a result,…
Dealing with limited overlap in estimation of average treatment effects
Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of…
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two…