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Title Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition Author info Martin Huber, Lukáš Lafférs Author Huber Martin (50%)
Co-authors Lafférs Lukáš 1986- (50%) UMBFP10 - Katedra matematiky
Source document Econometric Reviews. Vol. 41, no. 10 (2022), pp. 1141-1163. - New York : Taylor & Francis Group, 2022 Keywords kauzálne atribúcie mediačná služba analýza služieb výberové skúmanie - sample survey - survey sampling Form. Descr. články - journal articles Language English Country United States of America URL Link na zdrojový dokument Public work category ADC No. of Archival Copy 52387 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika 
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Title Causal mediation analysis with double machine learning Author info Helmut Farbmacher ... [et al.] Author Farbmacher Helmut (20%)
Co-authors Huber Martin (20%)
Lafférs Lukáš 1986- (20%) UMBFP10 - Katedra matematiky
Langen Henrika (20%)
Spindler Martin (20%)
Source document The Econometrics Journal. Vol. 25, no. 2 (2022), pp. 277-300. - Londýn : Royal Economic Society, 2022 Keywords matematické metódy - mathematical methods ekonomika - economics strojové učenie - machine learning analýza kauzálneho sprostredkovania - causal mediation analysis Form. Descr. články - journal articles Language English Country Great Britian Annotation 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 Public work category ADC No. of Archival Copy 51676 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika article
Title Evaluating (weighted) dynamic treatment effects by double machine learning Author info Hugo Bodory, Martin Huber, Lukáš Laffers Author Bodory Hugo (34%)
Co-authors Huber Martin (33%)
Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Source document The Econometrics Journal. Vol. 25, no. 3 (2022), pp. 628-648. - Londýn : Royal Economic Society, 2022 Keywords strojové učenie - machine learning intervencie Form. Descr. články - journal articles Language English Country Great Britian Annotation 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 Public work category ADC No. of Archival Copy 52191 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika article
Title Sharp IV bounds on average treatment effects on the treated and other populations under endogeneity and noncompliance Author info Martin Huber, Lukáš Lafférs, Giovanni Mellace Author Huber Martin (34%)
Co-authors Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Mellace Giovanni (33%)
Source document Journal of Applied Econometrics. Vol. 32, no. 1 (2017), pp. 56-79. - Hoboken : John Wiley & Sons, 2017 Keywords monotonicity random variables Language English Country United States of America systematics 51 Annotation 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. Public work category ADC No. of Archival Copy 39370 Repercussion category 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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