Výsledky vyhledávání - "Langen, Henrika"

Report

The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach

Autor: Langen, Henrika

This study assesses the effect of the #MeToo movement on the language used in judicial opinions on sexual violence related cases from 51 U.S. state and federal appellate courts. The study introduces various indicators to quantify the extent to which

Externí odkaz: http://arxiv.org/abs/2209.00409

Zobrazit plný text záznamu

Report

Detecting Grouped Local Average Treatment Effects and Selecting True Instruments

Autor: Apfel, Nicolas, Farbmacher, Helmut, Groh, Rebecca, Huber, Martin, Langen, Henrika

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, e

Externí odkaz: http://arxiv.org/abs/2207.04481

Zobrazit plný text záznamu

Report

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

Autor: Langen, Henrika, Huber, Martin

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. Besides assessing the average impacts of different types of coupons, we also investigate the he

Externí odkaz: http://arxiv.org/abs/2204.10820

Zobrazit plný text záznamu

Akademický článek

The impact of the #MeToo movement on language at court A text-based causal inference approach.

Autor: Langen, Henrika¹ (AUTHOR) rikalangen@t-online.de

Publikováno v: PLoS ONE. 5/15/2024, Vol. 19 Issue 5, p1-25. 25p.

Zobrazit plný text záznamu

Plný text ve formátu HTML

Report

The Impact of Response Measures on COVID-19-Related Hospitalization and Death Rates in Germany and Switzerland

Autor: Huber, Martin, Langen, Henrika

We assess the impact of COVID-19 response measures implemented in Germany and Switzerland on cumulative COVID-19-related hospitalization and death rates. Our analysis exploits the fact that the epidemic was more advanced in some regions than in other

Externí odkaz: http://arxiv.org/abs/2005.11278

Zobrazit plný text záznamu

Report

Causal mediation analysis with double machine learning

Autor: Farbmacher, Helmut, Huber, Martin, Lafférs, Lukáš, Langen, Henrika, Spindler, Martin

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

Externí odkaz: http://arxiv.org/abs/2002.12710

Zobrazit plný text záznamu