Lukas Laffers

Lukas Laffers
Univerzita Mateja Bela v Banskej Bystrici · Department of Mathematics

PhD

About

30
Publications
2,067
Reads
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119
Citations
Citations since 2016
25 Research Items
107 Citations
2016201720182019202020212022051015202530
2016201720182019202020212022051015202530
2016201720182019202020212022051015202530
2016201720182019202020212022051015202530
Additional affiliations
August 2009 - January 2014
NHH Norwegian School of Economics
Position
  • PhD Research Scholar

Publications

Publications (30)
Article
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment...
Article
Introduction: Slovakia is a country with the highest prevalence of liver cirrhosis in the world and a country with the highest proportion of Roma ethnicity at the same time. However, there is only little evidence of Roma representation in national cohorts with cirrhosis. Aims: 1. To determine the prevalence of Roma ethnicity in our cirrhosis and li...
Article
We consider evaluating the causal effects of dynamic treatments, i.e., of mul-tiple 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...
Article
Full-text available
Background: Non-alcoholic fatty liver disease (NAFLD) is the fastest-growing cause of liver diseases; after liver transplantation (LT) for another indications bears the name de novo NAFLD. Aims: We set out to determine the incidence of de novo NAFLD and its associations with BMI and fibrosis in patients (pts) after LT at a single transplant centre....
Article
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 w.r.t. misspecifications of the outcome, med...
Article
The problem of bounding average treatment effects under survey nonresponse, when data collection entails sequential efforts made to obtain response, can be formulated as an optimization problem. It is shown that this formulation is equivalent to the original problem and further extends it into a sensitivity analysis of the identifying assumptions....
Article
Full-text available
In the absence of effective vaccination, mass testing and quarantining of positive cases and their contacts could help to mitigate pandemics and allow economies to stay open. We investigate the effects of repeated mass testing on the COVID-19 pandemic caused by the SARS-CoV-2 virus, using data from the first ever nationwide rapid antigen testing im...
Article
Full-text available
Background: Physical frailty increases susceptibility to stressors and predicts adverse outcomes of cirrhosis. Data on disease course in different etiologies are scarce, so we aimed to compare the prevalence and risk factors of frailty and its impact on prognosis in nonalcoholic fatty liver (NAFLD) and alcoholic (ALD) cirrhosis. Patients and Metho...
Article
Full-text available
Chronic liver disease management is a comprehensive approach requiring multi-professional expertise and well-orchestrated healthcare measures thoroughly organized by responsible medical units. Contextually, the corresponding multi-faceted chain of healthcare events is likely to be severely disturbed or even temporarily broken under the force majeur...
Article
Full-text available
Purpose: Patients with advanced chronic liver disease (ACLD) often have a poor nutritional status. In the management, current guidelines recommend dietary counseling and oral nutritional supplements (ONS). Nutritional goals and adherence to ONS are difficult to achieve while studies addressing adherence are scarce. We aimed to evaluate adherence t...
Article
The impact of children's early development status on parental labor market outcomes is not well established in the empirical literature. We combine an instrumental variable approach to account for the endogeneity of the development status with a model of non‐random labor force participation to identify its impact. A one unit increase in our poor ch...
Preprint
Full-text available
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 i...
Preprint
Full-text available
This paper considers treatment evaluation when outcomes are only observed for a subpopulation due to sample selection or outcome attrition/non-response. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrit...
Preprint
Full-text available
This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on-observables assumption in a high-dimensional setting. We consider the average indirect effect of a binary treatment operating through an intermediate variable (or mediator) on the causal path betwe...
Preprint
Full-text available
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment...
Article
Full-text available
This paper presents a method of calculating sharp bounds on the average treatment effect using linear programming under identifying assumptions commonly used in the literature. This new method provides a sensitivity analysis of the identifying assumptions and missing data in two applications. The first application looks at the effect of parents’ sc...
Article
Full-text available
This paper provides a novel, simple, and computationally tractable method for determining an identified set that can account for a broad set of economic models when the economic variables are discrete. Using this method, we show using a simple example how imperfect instruments affect the size of the identified set when the assumption of strict exog...
Article
Full-text available
In this paper, we show that the testable implications derived in Huber and Mellace (Rev Econ Stat 97:398, 2015) are the best possible to detect invalid instruments in the presence of heterogeneous treatment effects and endogeneity. We also provide formal proof of the fact that those testable implications are only necessary, but not sufficient, cond...
Article
This paper reformulates the problem of bounding average treatment effects under sample selection studied in Lee (2009) as an optimization problem. This allows researchers to easily conduct sensitivity analyses of the identifying assumptions while the bounds remain sharp. We provide a mathematical formulation of the problem, replicate the existing a...
Article
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 inv...
Conference Paper
Full-text available
In this paper we address the question of human reading. We use an LDA based document classifier as a computational way to represent a person's mental state while she is engaged in reading. To illustrate the behaviour of our approach, we will analyze a corpus of climate change related blogs covering both sceptical and accepting positions in the deba...
Article
In this paper we show that the testable implications derived in Huber and Mellace (2013) are the best possible to detect invalid instruments, in the presence of heterogeneous treatment effects and endogeneity. We also provide a formal proof of the fact that those testable implications are only necessary but not sufficient conditions for instrument...
Article
Imposing the monotone treatment selection (MTS) assumption and the monotone instrumental variable (MIV) assumption implies bounds on average treatment effect that differ from those commonly reported in the applied literature. Instead, for the bounds to be correct, we should use an MTS assumption conditional on the value of a monotone instrument (cM...
Article
This paper provides a new simple and computationally tractable method for determining an identified set that can account for a broad set of economic models when economic variables are discrete. Using this method it is shown on a simple example how can imperfect instruments affect the size of the identified set when strict exogeneity is relaxed. It...
Article
Full-text available
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 inv...

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