Average Treatment Effect Bounds with an Instrumental Variable: Theory and Practice

Average Treatment Effect Bounds with an Instrumental Variable: Theory and Practice PDF

Author: Carlos A. Flores

Publisher: Springer

Published: 2018-09-29

Total Pages: 104

ISBN-13: 9811320179

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This book reviews recent approaches for partial identification of average treatment effects with instrumental variables in the program evaluation literature, including Manski’s bounds, bounds based on threshold crossing models, and bounds based on the Local Average Treatment Effect (LATE) framework. It compares these bounds across different sets of assumptions, surveys relevant methods to assess the validity of these assumptions, and discusses estimation and inference methods for the bounds. The book also reviews some empirical applications employing bounds in the program evaluation literature. It aims to bridge the gap between the econometric theory on which the different bounds are based and their empirical application to program evaluation.

Identification and Estimation of Local Average Treatment Effects

Identification and Estimation of Local Average Treatment Effects PDF

Author: Joshua D. Angrist

Publisher:

Published: 1995

Total Pages: 0

ISBN-13:

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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 and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score PDF

Author: Keisuke Hirano

Publisher:

Published: 2000

Total Pages: 68

ISBN-13:

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We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the pre-treatment variables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pre-treatment, the propensity score, also removes the entire bias associated with differences in pre-treatment variables. Thus it is possible to obtain unbiased estimates of the treatment effect without conditioning on a possibly high-dimensional vector of pre-treatment variables. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency. We show 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. This result holds whether the pre-treatment variables have discrete or continuous distributions. We provide intuition for this result in a number of ways. First we show that with discrete covariates, exact adjustment for the estimated propensity score is identical to adjustment for the pre-treatment variables. Second, we show that weighting by the inverse of the estimated propensity score can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score. Finally, we make a connection to results to other results on efficient estimation through weighting in the context of variable probability sampling.

Instrumental Variables

Instrumental Variables PDF

Author: James J. Heckman

Publisher:

Published: 2009

Total Pages: 0

ISBN-13:

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This paper considers the use of instrumental variables to estimate the mean effect of treatment on the treated, the mean effect of treatment on randomly selected persons and the local average treatment effect. It examines what economic questions these parameters address. When responses to treatment vary, the standard argument justifying the use of instrumental variables fails unless person-specific responses to treatment do not influence decisions to participate in the program being evaluated. This requires that individual gains from the program that cannot be predicted from variables in outcome equations do not influence the decision of the persons being studied to participate in the program. In the likely case in which individuals possess and act on private information about gains from the program that cannot be fully predicted by variables in the outcome equation, instrumental variables methods do not estimate economically interesting evaluation parameters. Instrumental variable methods are extremely sensitive to assumptions about how people process information. These arguments are developed for both continuous and discrete treatment variables, and several explicit economic models are presented.

Treatment Effect Heterogeneity in Theory and Practice

Treatment Effect Heterogeneity in Theory and Practice PDF

Author: Joshua David Angrist

Publisher:

Published: 2003

Total Pages: 30

ISBN-13:

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Instrumental Variables (IV) methods identify internally valid causal effects for individuals whose treatment status is manipulable by the instrument at hand. Inference for other populations requires homogeneity assumptions. This paper outlines a theoretical framework that nests causal homogeneity assumptions. These ideas are illustrated using sibling-sex composition to estimate the effect of child-bearing on economic and marital outcomes. The application is motivated by American welfare reform. The empirical results generally support the notion of reduced labor supply and increased poverty as a consequence of childbearing, but evidence on the impact of childbearing on marital stability and welfare use is more tenuous. Keywords: Instrumental Variables, Marital Stability, Welfare, Causal Effects. JEL Classification: C31, J12, J13.

Targeted Learning of Individual Effects and Individualized Treatments Using an Instrumental Variable

Targeted Learning of Individual Effects and Individualized Treatments Using an Instrumental Variable PDF

Author: Boriska Toth

Publisher:

Published: 2016

Total Pages: 124

ISBN-13:

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We consider estimation of causal effects when treatment assignment is potentially subject to unmeasured confounding, but a valid instrumental variable is available. Moreover, our models capture treatment effect heterogeneity, and we allow conditioning on an arbitrary subset of baseline covariates in estimating causal effects. We develop detailed methodology to estimate several types of quantities of interest: 1) the dose-response curve, where our parameter of interest is the projection unto a finite-dimensional working model; 2) the mean outcome under an optimal treatment regime, subject to a cost constraint; and 3) the mean outcome under an optimal intent-to-treat regime, subject to a cost constraint, in which an optimal intervention is done on the instrumental variable. These quantities have a central role for calculating and evaluating individualized treatment regimes. We use semiparametric modeling throughout and make minimal assumptions. Our estimate of the dose-response curve allows treatment to be continuous and makes slightly weaker assumptions than previous research. This work is the first to estimate the effect of an optimal treatment regime in the instrumental variables setting. For each of our parameters of interest, we establish identifiability, derive the efficient influence curve, and develop a new targeted minimum loss-based estimator (TMLE). In accordance with the TMLE methodology, these substitution estimators are asymptotically efficient and double robust. Detailed simulations confirm these desirable properties, and that our estimators can greatly outperform standard approaches. We also apply our estimator to a real dataset to estimate the effect of parents' education on their infant's health.

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