Výsledky vyhľadávania
Názov Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition Aut.údaje Martin Huber, Lukáš Lafférs Autor Huber Martin (50%)
Spoluautori Lafférs Lukáš 1986- (50%) UMBFP10 - Katedra matematiky
Zdroj.dok. Econometric Reviews. Vol. 41, no. 10 (2022), pp. 1141-1163. - New York : Taylor & Francis Group, 2022 Kľúč.slová kauzálne atribúcie mediačná služba analýza služieb výberové skúmanie - sample survey - survey sampling Form.deskr. články - journal articles Jazyk dok. angličtina Krajina Spojené štáty URL Link na zdrojový dokument Kategória publikačnej činnosti ADC Číslo archívnej kópie 52387 Katal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Báza dát xpca - PUBLIKAČNÁ ČINNOSŤ Odkazy PERIODIKÁ-Súborný záznam periodika 
článok
Názov Causal mediation analysis with double machine learning Aut.údaje Helmut Farbmacher ... [et al.] Autor Farbmacher Helmut (20%)
Spoluautori Huber Martin (20%)
Lafférs Lukáš 1986- (20%) UMBFP10 - Katedra matematiky
Langen Henrika (20%)
Spindler Martin (20%)
Zdroj.dok. The Econometrics Journal. Vol. 25, no. 2 (2022), pp. 277-300. - Londýn : Royal Economic Society, 2022 Kľúč.slová matematické metódy - mathematical methods ekonomika - economics strojové učenie - machine learning analýza kauzálneho sprostredkovania - causal mediation analysis Form.deskr. články - journal articles Jazyk dok. angličtina Krajina Veľká Británia Anotácia This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are robust with respect to misspecifications of the outcome, mediator, and treatment models. This property is key for selecting these models by double machine learning, which is combined with data splitting to prevent overfitting. We demonstrate that the effect estimators are asymptotically normal and n−1/2-consistent under specific regularity conditions and investigate the finite sample properties of the suggested methods in a simulation study when considering lasso as machine learner. We also provide an empirical application to the US National Longitudinal Survey of Youth, assessing the indirect effect of health insurance coverage on general health operating via routine checkups as mediator, as well as the direct effect. URL Link na plný text Kategória publikačnej činnosti ADC Číslo archívnej kópie 51676 Katal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Báza dát xpca - PUBLIKAČNÁ ČINNOSŤ Odkazy PERIODIKÁ-Súborný záznam periodika článok
Názov Evaluating (weighted) dynamic treatment effects by double machine learning Aut.údaje Hugo Bodory, Martin Huber, Lukáš Laffers Autor Bodory Hugo (34%)
Spoluautori Huber Martin (33%)
Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Zdroj.dok. The Econometrics Journal. Vol. 25, no. 3 (2022), pp. 628-648. - Londýn : Royal Economic Society, 2022 Kľúč.slová strojové učenie - machine learning intervencie Form.deskr. články - journal articles Jazyk dok. angličtina Krajina Veľká Británia Anotácia We consider evaluating the causal effects of dynamic treatments, i.e.. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high-dimensional covariates. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups. e.g.. among those treated in the first treatment period. We demonstrate that the estimators are asymptotically normal and root n-consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study. URL Link na zdrojový dokument Kategória publikačnej činnosti ADC Číslo archívnej kópie 52191 Katal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Báza dát xpca - PUBLIKAČNÁ ČINNOSŤ Odkazy PERIODIKÁ-Súborný záznam periodika článok
Názov Sharp IV bounds on average treatment effects on the treated and other populations under endogeneity and noncompliance Aut.údaje Martin Huber, Lukáš Lafférs, Giovanni Mellace Autor Huber Martin (34%)
Spoluautori Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Mellace Giovanni (33%)
Zdroj.dok. Journal of Applied Econometrics. Vol. 32, no. 1 (2017), pp. 56-79. - Hoboken : John Wiley & Sons, 2017 Kľúč.slová monotonicity random variables Jazyk dok. angličtina Krajina Spojené štáty Systematika 51 Anotácia In the presence of an endogenous binary treatment and a valid binary instrument, causal effects are point identified only for the subpopulation of compliers, given that the treatment is monotone in the instrument. With the exception of the entire population, causal inference for further subpopulations has been widely ignored in econometrics. We invoke treatment monotonicity and/or dominance assumptions to derive sharp bounds on the average treatment effects on the treated, as well as on other groups. Furthermore, we use our methods to assess the educational impact of a school voucher program in Colombia and discuss testable implications of our assumptions. Copyright (C) 2015 John Wiley & Sons, Ltd. Kategória publikačnej činnosti ADC Číslo archívnej kópie 39370 Kategória ohlasu FLORES, Carlos. A. - CHEN, Xuan. Average treatment effect bounds with an instrumental variable : theory and practice. Singapore : Springer Singapore, 2018. 104 p. ISBN 978-981-13-2016-3.
SWANSON, Sonja A. - HERNAN, Miguel A. - MILLER, Matthew - ROBINS, James M. - RICHARDSON, Thomas S. Partial identification of the average treatment effect using instrumental variables : review of methods for binary instruments, treatments, and outcomes. In Journal of the American statistical association. ISSN 0162-1459, 2018, vol. 113, no. 522, pp. 933-947.
DEPALO, Domenico. Identification issues in the public/private wage gap, with an application to Italy. In Journal of applied econometrics. ISSN 0883-7252, 2018, vol. 33, no. 3, pp. 435-456.
CHEN, Xuan - FLORES, Carlos A. - FLORES-LAGUNES, Alfonso. Going beyond LATE: bounding average treatment effects of job corps training. In Journal of human resources. ISSN 0022-166X, 2018, vol. 53, no. 4, pp. 1050-1099.
LIU, Lan - MIAO, Wang - SUN, Baoluo - ROBINS, James - TCHETGEN, Eric Tchetgen. Identification and inference for marginal average treatment effect on the treated with an instrumental variable. In Statistica sinica. ISSN 1017-0405, 2020, vol. 30, no. 3, pp. 1517-1541.
KITAGAWA, Toru. The identification region of the potential outcome distributions under instrument independence. In Journal of econometrics. ISSN 0304-4076, 2021, vol. 225, no. 2, special issue, pp. 231-253.
WANG, Xintong - FLORES-LAGUNES, Alfonso. Conscription and military service do they result in future violent and nonviolent incarcerations and recidivism. In Journal of human resources. ISSN 0022-166X, 2022, vol. 57, no. 5, pp. 1715-1757.
KÉDAGNI, Désiré. Identifying treatment effects in the presence of confounded types. In Journal of econometrics. ISSN 0304-4076, 2023, vol. 234, no. 2, pp. 479-511.
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