Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes. Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attribute has no (direct or indirect) causal effect on the target. We propose rank-preserving interventional distributions to define an estimand of this FiND world and a warping method for estimation. Evaluation criteria for both the method and resulting model are presented and validated through simulations and empirical data. With this, we show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness

Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpar

„Kriseninternes Lernen“ und „krisenübergreifendes Lernen“ in der deutschen Kommunalverwaltung

Published online: 15 June 2022Krisen testen die Leistungsfähigkeit von Verwaltungen unter Realbedingungen. Vor diesem Hintergrund analysiert der vorliegende Beitrag Reaktion der deutschen Kommunalverwaltung auf die Fluchtmigration zwischen 2015 und 2017 und auf die erste Welle der COVID-19-Pandemie in 2020. Mit Blick auf die Debatte zum organisationalen Lernen in Ausnahmesituationen liegt der Schwerpunkt der Analyse auf der Rolle administrativer Netzwerke sowie der Lernfähigkeit von öffentlichen Behörden während sowie zwischen Krisensituationen. Die Auswertung zweier Umfragen unter Mitarbeitern der deutschen Kommunalverwaltung zeigt erstens, dass die Qualität der verwaltungsinternen und der zivilgesellschaftlichen Vernetzung von zentraler Bedeutung für administrative Krisenperformanz sind. Zweitens korrespondiert Leistungsfähigkeit in Krisen mit der Bereitschaft sowie mit der Fähigkeit, Lehren aus früheren Krisen zu ziehen.Crises constitute a test of the efficiency of the administrations under real conditions. This is where this article departs, comparatively analyzing the recent “migration crisis” and first wave of the COVID pandemic. Against the backdrop of the debate on organizational learning of public administration in exceptional situations, the analysis focuses on the role of administrative networks and the ability to learn during and between crises. The evaluation of two surveys among employees of German local government shows firstly that the quality of networking within the administration and civil society is of central importance for administrative crisis performance. Second, successful crisis performance corresponds to the willingness and organizational ability to draw lessons from previous crises.This article was published Open Access with the support from the EUI Library through the CRUI - Springer Transformative Agreement (2020-2024

Gender differences in survival among adult patients starting antiretroviral therapy in South Africa: a multicentre cohort study.

Increased mortality among men on antiretroviral therapy (ART) has been documented but remains poorly understood. We examined the magnitude of and risk factors for gender differences in mortality on ART

Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions

Longitudinal targeted maximum likelihood estimation (LTMLE) has hardly ever been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, i.e. it is doubly robust, and can yield valid inference when used in conjunction with machine learning. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings and compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies 1) long follow-up time (30 months), 2) gradually declining sample size 3) limited support for some intervention rules of interest 4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: we show that (i) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (ii) machine learning utilized with a small set of simple learners (if more complex ones can’t be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; (iii) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis