Search Results for author: Martin Huber

Found 23 papers, 5 papers with code

Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity

1 code implementation18 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.

Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

1 code implementation24 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.

Image Registration Imitation Learning

Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing

no code implementations21 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.

reinforcement-learning

Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning

no code implementations3 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.

Treatment Effect Analysis for Pairs with Endogenous Treatment Takeup

no code implementations12 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.

Causal Inference

The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates

no code implementations14 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.

regression

Detecting Grouped Local Average Treatment Effects and Selecting True Instruments

no code implementations10 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.

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

no code implementations22 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.

Marketing

Testing the identification of causal effects in observational data

no code implementations29 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.

valid

From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market outcomes

no code implementations29 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.

Homography-based Visual Servoing with Remote Center of Motion for Semi-autonomous Robotic Endoscope Manipulation

1 code implementation25 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.

Image Registration

Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction

2 code implementations30 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%.

Homography Estimation Imitation Learning +1

Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data

no code implementations8 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.

BIG-bench Machine Learning

How residence permits affect the labor market attachment of foreign workers: Evidence from a migration lottery in Liechtenstein

no code implementations25 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.

Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets

no code implementations4 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.

BIG-bench Machine Learning

Deep learning for detecting bid rigging: Flagging cartel participants based on convolutional neural networks

no code implementations22 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.

The fiscal response to revenue shocks

no code implementations19 Jan 2021 Simon Berset, Martin Huber, Mark Schelker

We study the impact of fiscal revenue shocks on local fiscal policy.

BIG-bench Machine Learning

Evaluating (weighted) dynamic treatment effects by double machine learning

no code implementations1 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.

BIG-bench Machine Learning

Double machine learning for sample selection models

no code implementations30 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.

BIG-bench Machine Learning

A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels

no code implementations12 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.

BIG-bench Machine Learning

An introduction to flexible methods for policy evaluation

no code implementations1 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.

Causal Inference

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