activity
20192026
most citedDouble machine learning for sample selection models

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ME2026

Correcting for Nonignorable Nonresponse Bias in Ordinal Observational Survey Data

Lukáš Lafférs, Jozef Michal Mintal, Ivan Sutóris

Many political surveys rely on post-stratification, raking, or related weighting adjustments to align respondents with the target population. But when respondents differ from nonre…

econ.EM2024

Testing identification in mediation and dynamic treatment models

Martin Huber, Kevin Kloiber, Lukas Laffers

We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be contro…

econ.EM2020★ 3 cited

Double machine learning for sample selection models

Michela Bia, Martin Huber, Lukáš Lafférs

This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For ident…

econ.EM2020

Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition

Martin Huber, Lukáš Lafférs

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…

nucl-th2019

Benchmark values for the net proton number fluctuations

Boris Tomasik, Ivan Melo, Lukas Laffers +1

We formulate a baseline model for the net-proton number fluctuations in ultrarelativistic heavy-ion collisions. Our model includes total baryon number conservation, fluctuations of…