Structural Racism and Quantitative Causal Inference: A Life Course Mediation Framework for Decomposing Racial Health Disparities

Quantitative studies of racial health disparities often use static measures of self-reported race and conventional regression estimators, which critics argue is inconsistent with social-constructivist theories of race, racialization, and racism. We demonstrate an alternative counterfactual approach to explain how multiple racialized systems dynamically shape health over time, examining racial inequities in cardiometabolic risk in the National Longitudinal Study of Adolescent to Adult Health. This framework accounts for the dynamics of time-varying confounding and mediation that is required in operationalizing a “race” variable as part of a social process ( racism) rather than a separable, individual characteristic. We decompose the observed disparity into three types of effects: a controlled direct effect (“unobserved racism”), proportions attributable to interaction (“racial discrimination”), and pure indirect effects (“emergent discrimination”). We discuss the limitations of counterfactual approaches while highlighting how they can be combined with critical theories to quantify how interlocking systems produce racial health inequities.

When less conditioning provides better estimates: overcontrol and endogenous selection biases in research on intergenerational mobility

The counterfactual approach to causality has become the dominant approach to understand causality in contemporary social science research. Whilst most sociologists are aware that unobserved, confounding variables may bias the estimates of causal effects (omitted variable bias), the threats of overcontrol and endogenous selection biases are less well known. In particular, widely used practices in research on intergenerational mobility are affected by these biases. I review four of these practices from the viewpoint of the counterfactual approach to causality and show why overcontrol and endogenous selection biases arise when these practices are implemented. I use data from the German Socio-Economic Panel Study (SOEP) to demonstrate the practical consequences of these biases for conclusions about intergenerational mobility. I conclude that future research on intergenerational mobility should reflect more upon the possibilities of bias introduced by conditioning on variables.

Historical Injustice in Immigration Policy

The history of immigration policy is marked by the wrongful and discriminatory exclusion of certain groups of people. In this article, I argue that descendants of those who were wrongfully excluded have a pro tanto right to immigrate to the state in question as reparation. I begin by identifying the two main approaches theorists generally take to establish a claim for reparation: the inheritance approach and the counterfactual approach. In the first section, I argue that the inheritance approach does not offer a promising argument for reparations for descendants of those who were wrongfully excluded. In the second section, I argue that the counterfactual approach, by contrast, does. In the third section, I respond to the objection that this prima facie claim for reparation can be undermined by current circumstances. In the fourth section, I show why this reparation should be offered in the form of immigration rights.

The rise in youth employment? Impact evaluation of COVID-19 consequences

The COVID-19 pandemic has significant consequences in many areas and has largely contributed to rising unemployment in almost all countries. The situation is similar in Slovakia, where various degrees of shut-down measures have caused an inflow of newly unemployed people in all age groups. This article focuses on unemployed youth as a vulnerable and disadvantaged group of the working population, as starting a career during a pandemic is complicated or even impossible in some industries under these conditions. In this study, the real situation in Slovakia in 2020-2021 is compared with the development of the job market before the pandemic. The main contribution of the study is the quantification of the extent of the pandemic impact on unemployed youth, but also on other age groups; this evaluation can be considered very accurate given the counterfactual approach used. The results of this study can be used in practice to identify the most affected groups of the population and to implement policy measures aimed at mitigating the effects of the pandemic, and then to adjust the intensity and amount of allocated funds that will be needed to be spent to support the placement of young people into the labor market.

Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling

The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others’ policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country’s first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.

Effect of social deprivation on peritoneal dialysis uptake: A mediation analysis with the data of the REIN registry

Background: Social deprivation could act as a barrier to peritoneal dialysis (PD). The objective of this study was to assess the association between social deprivation estimated by the European deprivation index (EDI) and PD uptake and to explore the potential mediators of this association. Methods: From the Renal Epidemiology and Information Network registry, patients who started dialysis in 2017 were included. The EDI was calculated based on the patient’s address. The event of interest was the proportion of PD 3 months after dialysis initiation. A mediation analysis with a counterfactual approach was carried out to evaluate the direct and indirect effect of the EDI on the proportion of PD. Results: Among the 9588 patients included, 1116 patients were on PD; 2894 (30.2%) patients belonged to the most deprived quintile (Q5). PD was associated with age >70 years (odds ratio (OR) 0.79 [95% confidence interval (CI): 0.69–0.91]), male gender (0.85 [95% CI: 0.74–0.97]), cardiovascular disease (OR 0.86 [95% CI: 0.86–1.00]), chronic heart failure (OR 1.34 [95% CI: 1.13–1.58]), active cancer (OR 0.67 [95% CI: 0.53–0.85]) and obesity (OR 0.75 [95% CI: 0.63–0.89]). In the mediation analysis, Q5 had a direct effect on PD proportion OR 0.84 [95% CI: 0.73–0.96]. The effect of Q5 on the proportion of PD was mediated by haemoglobin level at dialysis initiation (OR 0.96 [95% CI: 0.94–0.98]) and emergency start (OR 0.98 [95% CI: 0.96–0.99]). Conclusion: Social deprivation, estimated by the EDI, was associated with a lower PD uptake. The effect of social deprivation was mediated by haemoglobin level, a proxy of predialysis care and emergency start.

Explaining the Black-box Smoothly- A Counterfactual Approach

<p>We propose a BlackBox <i>Counterfactual Explainer</i> that is explicitly developed for medical imaging applications. Classical approaches (<i>e.g.,</i> saliency maps) assessing feature importance do not explain <i>how</i> and <i>why</i> variations in a particular anatomical region are relevant to the outcome, which is crucial for transparent decision making in healthcare application. Our framework explains the outcome by gradually <i>exaggerating</i> the semantic effect of the given outcome label. Given a query input to a classifier, Generative Adversarial Networks produce a progressive set of perturbations to the query image that gradually changes the posterior probability from its original class to its negation. We design the loss function to ensure that essential and potentially relevant details, such as support devices, are preserved in the counterfactually generated images. We provide an extensive evaluation of different classification tasks on the chest X-Ray images. Our experiments show that a counterfactually generated visual explanation is consistent with the disease's clinical relevant measurements, both quantitatively and qualitatively.</p>