Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score PDF Download
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Author: Keisuke Hirano
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983a) show that adjusting solely for differences between treated and control units in a scalar function of the covariates, the propensity score, also removes all biases associated with differences in covariates. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, Todd (1998), and Rotnitzky and Robins (1995). We show that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to efficient estimates of the average treatment effect. We provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score.
Author: Keisuke Hirano
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983a) show that adjusting solely for differences between treated and control units in a scalar function of the covariates, the propensity score, also removes all biases associated with differences in covariates. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, Todd (1998), and Rotnitzky and Robins (1995). We show that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to efficient estimates of the average treatment effect. We provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score.
Author: Markus Frölich
Publisher:
ISBN:
Category :
Languages : en
Pages : 28
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Book Description
Author: Ying-Ying Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
Matching is a widely used program evaluation estimation method when treatment is assigned at random conditional on observable characteristics. When a multivalued treatment takes on more than two values, valid causal comparisons for a subpopulation who is treated a particular treatment level are based on two propensity scores - one for the treated level and one for the counterfactual level. The main contribution of this paper is propensity score regression estimators for a class of treatment effects for the treated that achieve the semiparametric efficiency bounds under the cases when the propensity scores are unknown and when they are known. We derive the large sample distribution that accounts for the estimation error of the propensity score as generated regressors. We contribute to the binary treatment literature by a new propensity score regression estimator for the average/quantile treatment effect for the treated: our efficient estimator matches on a normalized propensity score that is a combination of the true propensity score and its nonparametric estimate. There are two key findings: (I) The efficiency bound is reduced by knowledge of the propensity scores for the treated levels, but is not affected by knowledge of the propensity score for the counterfactual level. (II) Matching on the nonparametrically estimated propensity score recovers the information contained in matching on the pretreatment variables.
Author: Steven Durlauf
Publisher: Springer
ISBN: 0230280811
Category : Literary Criticism
Languages : en
Pages : 365
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Book Description
Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Author: Kip Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
Background: The increased availability of claims data allows one to build high dimensional datasets, rich in covariates, for accurately estimating treatment effects in medical and epidemiological cohort studies. This paper shows the full potential of machine learning for the estimation of average treatment effects with propensity score methods in a context rich and high dimensional datasets. Methods: Four different methods are used to estimate average treatment effects in the context of time to event outcomes. The four methods explored in this study are LASSO, Random Forest, Gradient Descent Boosting and Artificial Neural networks. Simulations based on an actual medical claims data set are used to assess the efficiency of these methods. The simulations are performed with over 100, 000 observations and 1,100 explanatory variables. Each method is tested on 500 datasets that are created from the original dataset, allowing us to report the mean and standard deviation of estimated average treatment effects. Results: The results are very promising for all four methods; however, LASSO, Random Forest and Gradient Boosting seem to be performing better than Random Forest. Conclusion: Machine Learning methods can be helpful for observational studies that use the propensity score when a very large number of covariates are available, the total number of observations is large, and the dependent event rare. This is an important result given the availability of big data related to Health Economics and Outcomes Research (HEOR) around the world.
Author: G. G. Lorentz
Publisher: American Mathematical Society
ISBN: 1470474948
Category : Mathematics
Languages : en
Pages : 200
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Book Description
This is an easily accessible account of the approximation of functions. It is simple and without unnecessary details, but complete enough to include the classical results of the theory. With only a few exceptions, only functions of one real variable are considered. A major theme is the degree of uniform approximation by linear sets of functions. This encompasses approximations by trigonometric polynomials, algebraic polynomials, rational functions, and polynomial operators. The chapter on approximation by operators does not assume extensive knowledge of functional analysis. Two chapters cover the important topics of widths and entropy. The last chapter covers the solution by Kolmogorov and Arnol?d of Hilbert's 13th problem. There are notes at the end of each chapter that give information about important topics not treated in the main text. Each chapter also has a short set of challenging problems, which serve as illustrations.
Author: Keisuke Hirano
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 68
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Book Description
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.
Author: Wei Pan
Publisher: Guilford Publications
ISBN: 1462519490
Category : Psychology
Languages : en
Pages : 417
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Book Description
This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).
Author:
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 46
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Book Description
Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing such lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1,0.9]
Author: Sungur Gurel
Publisher:
ISBN:
Category :
Languages : en
Pages : 61
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Book Description
We investigated the performance of four different propensity score (PS) methods to reduce selection bias in estimates of the average treatment effect (ATE) in observational studies: inverse probability of treatment weighting (IPTW), truncated inverse probability of treatment weighting (TIPTW), optimal full propensity score matching (OFPSM), and propensity score stratification (PSS). We compared these methods in combination with three methods of standard error estimation: weighted least squares regression (WLS), Taylor series linearization (TSL), and jackknife (JK). We conducted a Monte Carlo Simulation study manipulating the number of subjects and the ratio of treated to total sample size. The results indicated that IPTW and OFPSM methods removed almost all of the bias while TIPTW and PSS removed about 90% of the bias. Some of TSL and JK standard errors were acceptable, some marginally overestimated, and some moderately overestimated. For the lower ratio of treated on sample sizes, all of the WLS standard errors were strongly underestimated, as designs get balanced, the underestimation gets less serious. Especially for the OFPSM, all of the TSL and JK standard errors were overestimated and WLS standard errors under estimated under all simulated conditions.
