1 code implementation • 18 Oct 2023 • Julia Hatamyar, Noemi Kreif, Rudi Rocha, Martin Huber
We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using machine learning.
1 code implementation • 24 Jul 2023 • Martin Huber, Sebastien Ourselin, Christos Bergeles, Tom Vercauteren
In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions.
no code implementations • 21 Jul 2023 • Charlie Budd, Jianrong Qiu, Oscar MacCormac, Martin Huber, Christopher Mower, Mirek Janatka, Théo Trotouin, Jonathan Shapey, Mads S. Bergholt, Tom Vercauteren
In addition, we performed a blinded usability trial by having two neurosurgeons compare the system with different autofocus policies, and found our novel approach to be the most favourable, making our system a desirable addition for intraoperative HSI.
no code implementations • 3 Jul 2023 • Yu-Chin Hsu, Martin Huber, Yu-Min Yen
We suggest double/debiased machine learning estimators of direct and indirect quantile treatment effects under a selection-on-observables assumption.
no code implementations • 12 Jan 2023 • Mate Kormos, Robert P. Lieli, Martin Huber
We study causal inference in a setting in which units consisting of pairs of individuals (such as married couples) are assigned randomly to one of four categories: a treatment targeted at pair member A, a potentially different treatment targeted at pair member B, joint treatment, or no treatment.
no code implementations • 14 Dec 2022 • Hugo Bodory, Martin Huber, Michael Lechner
This paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed set of covariates to evaluate the local average treatment effect.
1 code implementation • 26 Oct 2022 • Charlie Budd, Luis C. Garcia-Peraza-Herrera, Martin Huber, Sebastien Ourselin, Tom Vercauteren
The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines.
no code implementations • 10 Jul 2022 • Nicolas Apfel, Helmut Farbmacher, Rebecca Groh, Martin Huber, Henrika Langen
Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local average treatment effects (LATE) despite relying on distinct instruments, even if several instruments violate the identifying assumptions.
no code implementations • 22 Apr 2022 • Henrika Langen, Martin Huber
We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer.
no code implementations • 29 Mar 2022 • Martin Huber, Jannis Kueck
This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two sets of variables: observed covariates to be controlled for and a suspected instrument.
no code implementations • 29 Nov 2021 • Selina Gangl, Martin Huber
We analyse the effect of mandatory kindergarten attendance for four-year-old children on maternal labour market outcomes in Switzerland.
1 code implementation • 25 Oct 2021 • Martin Huber, John Bason Mitchell, Ross Henry, Sébastien Ourselin, Tom Vercauteren, Christos Bergeles
Our approach allows a surgeon to build a graph of desired views, from which, once built, views can be manually selected and automatically servoed to irrespective of robot-patient frame transformation changes.
no code implementations • 21 Oct 2021 • Imanol Luengo, Maria Grammatikopoulou, Rahim Mohammadi, Chris Walsh, Chinedu Innocent Nwoye, Deepak Alapatt, Nicolas Padoy, Zhen-Liang Ni, Chen-Chen Fan, Gui-Bin Bian, Zeng-Guang Hou, Heonjin Ha, Jiacheng Wang, Haojie Wang, Dong Guo, Lu Wang, Guotai Wang, Mobarakol Islam, Bharat Giddwani, Ren Hongliang, Theodoros Pissas, Claudio Ravasio, Martin Huber, Jeremy Birch, Joan M. Nunez Do Rio, Lyndon Da Cruz, Christos Bergeles, Hongyu Chen, Fucang Jia, Nikhil KumarTomar, Debesh Jha, Michael A. Riegler, Pal Halvorsen, Sophia Bano, Uddhav Vaghela, Jianyuan Hong, Haili Ye, Feihong Huang, Da-Han Wang, Danail Stoyanov
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
2 code implementations • 30 Sep 2021 • Martin Huber, Sébastien Ourselin, Christos Bergeles, Tom Vercauteren
We perform an extensive evaluation of state-of-the-art (SOTA) Deep Neural Networks (DNNs) across multiple compute regimes, finding our method transfers from our camera motion free da Vinci surgery dataset to videos of laparoscopic interventions, outperforming classical homography estimation approaches in both, precision by 41%, and runtime on a CPU by 43%.
no code implementations • 8 Jun 2021 • Yu-Chin Hsu, Martin Huber, Ying-Ying Lee, Chu-An Liu
While most treatment evaluations focus on binary interventions, a growing literature also considers continuously distributed treatments.
no code implementations • 25 May 2021 • Berno Buechel, Selina Gangl, Martin Huber
We analyze the impact of obtaining a residence permit on foreign workers' labor market and residential attachment.
no code implementations • 4 May 2021 • Martin Huber, Jonas Meier, Hannes Wallimann
Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer- or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking a trip otherwise not realized by train, buying a first- rather than second-class ticket, or rescheduling a trip (e. g.\ away from rush hours) when being offered a supersaver ticket.
no code implementations • 22 Apr 2021 • Martin Huber, David Imhof
Based on Japanese and Swiss procurement data, we construct such graphs for both collusive and competitive episodes (i. e when a bid-rigging cartel is or is not active) and use a subset of graphs to train the neural network such that it learns distinguishing collusive from competitive bidding patterns.
no code implementations • 19 Jan 2021 • Simon Berset, Martin Huber, Mark Schelker
We study the impact of fiscal revenue shocks on local fiscal policy.
no code implementations • 1 Dec 2020 • Hugo Bodory, Martin Huber, Lukáš Lafférs
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.
no code implementations • 30 Nov 2020 • 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.
no code implementations • 12 Apr 2020 • Hannes Wallimann, David Imhof, Martin Huber
We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels.
no code implementations • 1 Oct 2019 • Martin Huber
This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest.