Research Group Michael Schomaker

Positivity violations pose a key challenge in the estimation of causal effects, particularly for continuous interventions. Current approaches for addressing this issue include the use of projection functions or modified treatment policies. While effective in many contexts, these methods can result in estimands that potentially do not align well with the original research question, thereby leading to compromises in interpretability. In this paper, we introduce a novel diagnostic tool, the non-overlap ratio, to detect positivity violations. To address these violations while maintaining interpretability, we propose a data-adaptive solution, specially a ‘most feasible’ intervention strategy. Our strategy operates on a unit-specific basis. For a given intervention of interest, we first assess whether the intervention value is feasible for each unit. For units with sufficient support, conditional on confounders, we adhere to the intervention of interest. However, for units lacking sufficient support, as identified through the assessment of the non-overlap ratio, we do not assign the actual intervention value of interest. Instead, we assign the closest feasible value within the support region. We propose an estimator using g-computation coupled with flexible conditional density estimation to estimate high- and low support regions to estimate this new estimand. Through simulations, we demonstrate that our method effectively reduces bias across various scenarios by addressing positivity violations. Moreover, when positivity violations are absent, the method successfully recovers the standard estimand. We further validate its practical utility using real-world data from the CHAPAS-3 trial, which enrolled HIV-positive children in Zambia and Uganda.

A growing body of literature in fairness-aware ML aspires to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM) by defining metrics that measure the fairness of an ML model and by proposing methods that ensure that trained ML models achieve low values in those metrics (see, e.g., Verma & Rubin, 2018, Caton & Haas, 2023). However, the underlying concept of fairness, i.e., the question of what fairness is, is rarely discussed, leaving a considerable gap between centuries of philosophical discussion and the recent adoption of the concept in the ML community. We bridge this gap by formalizing a consistent concept of fairness and translating the philosophical considerations into a formal framework for training and evaluating ML models in ADM systems (Bothmann et al., 2024). We argue why and how causal considerations are necessary when assessing fairness in the presence of protected attributes (PAs) by proposing a fictitious, normatively desired (FiND) world where the PAs have no (direct or indirect) causal effect on the target. In practice, this unknown FiND world must be approximated by a warped world, for which the causal effects of the PAs must be removed from the real-world data. We propose rank-preserving interventional distributions to define an estimand of this FiND world and a warping method for estimation (Bothmann et al., 2023). Evaluation criteria for both the method and the resulting ML model are presented. Experiments on simulated data show that our method effectively identifies the most discriminated individuals and mitigates unfairness. Experiments on real-world data showcase the practical application of our method.

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.