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Outliers in semi-parametric estimation of treatment effects
Ontiveros, Darwin Ugarte; Canavire-Bacarreza, Gustavo; … - 2017
Average treatment effects estimands can present significant bias under the presence of outliers. Moreover, outliers can … outliers. Bad and good leverage points outliers are considered. The bias arises because bad leverage points completely change … the propensity score. We provide some clues to diagnose the presence of outliers and propose a reweighting estimator that …
Persistent link: https://www.econbiz.de/10011778870
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Outliers in semi-parametric estimation of treatment effects
Canavire-Bacarreza, Gustavo; Castro, Luis; Ontiveros, … - In: Econometrics : open access journal 9 (2021) 2, pp. 1-32
Outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric estimates. In this … presence of outliers. Bad and good leverage point outliers are considered. Bias arises in the case of bad leverage points … correct for the effects of outliers following a reweighting strategy in the spirit of the Stahel-Donoho (SD) multivariate …
Persistent link: https://www.econbiz.de/10012547410
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Model averaging in semiparametric estimation of treatment effects
Kitagawa, Toru; Muris, Chris - 2015
In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice that the researchers make when estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over the candidate specifications...
Persistent link: https://www.econbiz.de/10011309717
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New evidence on the finite sample properties of propensity score matching and reweighting estimators
Busso, Matias; DiNardo, John; McCrary, Justin - 2009
Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting...
Persistent link: https://www.econbiz.de/10010268994
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The value of knowing the propensity score for estimating average treatment effects
Rothe, Christoph - 2016
In a treatment effect model with unconfoundedness, treatment assignments are not only independent of potential outcomes given the covariates, but also given the propensity score alone. Despite this powerful dimension reduction property, adjusting for the propensity score is known to lead to an...
Persistent link: https://www.econbiz.de/10011486511
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New evidence on linear regression and treatment effect heterogeneity
Słoczyński, Tymon - 2015
It is standard practice in applied work to rely on linear least squares regression to estimate the effect of a binary variable ("treatment") on some outcome of interest. In this paper I study the interpretation of the regression estimand when treatment effects are in fact heterogeneous. I show...
Persistent link: https://www.econbiz.de/10011387124
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A martingale representation for matching estimators
Abadie, Alberto; Imbens, Guido W. - 2009
Matching estimators are widely used in statistical data analysis. However, the distribution of matching estimators has been derived only for particular cases (Abadie and Imbens, 2006). This article establishes a martingale representation for matching estimators. This representation allows the...
Persistent link: https://www.econbiz.de/10010269062
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Minimizing bias in selection on observables estimators when unconfoundness fails
Millimet, Daniel L.; Tchernis, Rusty - 2008
We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using...
Persistent link: https://www.econbiz.de/10010268598
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Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - 2019
Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a...
Persistent link: https://www.econbiz.de/10012060603
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Interpreting OLS estimands when treatment effects are heterogeneous : smaller groups get larger weights
Słoczyński, Tymon - 2020
Applied work often studies the effect of a binary variable (“treatment”) using linear models with additive effects. I study the interpretation of the OLS estimands in such models when treatment effects are heterogeneous. I show that the treatment coefficient is a convex combination of two...
Persistent link: https://www.econbiz.de/10012223869