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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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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; … - 2020
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/10012165548
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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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Outliers in Semi-Parametric Estimation of Treatment Effects
Ugarte Ontiveros, Darwin - 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/10012944434
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Testing exogeneity in cross-section regression by sorting data
DeLuna, Xavier; Johansson, Per-Olov - 2000
We introduce a framework to test for exogeneity of a variable in a regression based on cross-sectional data. By sorting data with respect to a function (sorting score) of known exogeneous variables it is possible to utilize a battery of tools originally develped to detecting model...
Persistent link: https://www.econbiz.de/10011574988
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Semiparametrically efficient estimation of the average linear regression function
Graham, Bryan S.; Pinto, Cristine Campos de Xavier - 2018
Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive "treatments". Consider the linear regression of Y onto X in a subpopulation homogenous in...
Persistent link: https://www.econbiz.de/10011924562
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Model misspecification and bias for inverse probability weighting and doubly robust estimators
Waernbaum, Ingeborg; Pazzagli, Laura - 2017
In the causal inference literature a class of semi-parametric estimators is called robust if the estimator has desirable properties under the assumption that at least one of the working models is correctly specified. A standard example is a doubly robust estimator that specifies parametric...
Persistent link: https://www.econbiz.de/10011796394
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Semiparametric estimation and inference using doubly robust moment conditions
Rothe, Christoph; Firpo, Sergio - 2013
We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step, but retain a fully nonparametric specification in the first step. Such estimators exist in many economic applications, including a wide range of missing data and...
Persistent link: https://www.econbiz.de/10009792511